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  • 1.
    Ahlinder, Jon
    et al.
    Totalförsvarets Forskningsinstitut, FOI, Stockholm, Sweden.
    Nordgaard, Anders
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences. Swedish National Forensic Centre (NFC), Linköping, Sweden.
    Wiklund Lindström, Susanne
    Totalförsvarets Forskningsinstitut, FOI, Stockholm, Sweden.
    Chemometrics comes to court: evidence evaluation of chem–bio threat agent attacks2015In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 29, no 5, 267-276 p.Article in journal (Refereed)
    Abstract [en]

    Forensic statistics is a well-established scientific field whose purpose is to statistically analyze evidence in order to support legal decisions. It traditionally relies on methods that assume small numbers of independent variables and multiple samples. Unfortunately, such methods are less applicable when dealing with highly correlated multivariate data sets such as those generated by emerging high throughput analytical technologies. Chemometrics is a field that has a wealth of methods for the analysis of such complex data sets, so it would be desirable to combine the two fields in order to identify best practices for forensic statistics in the future. This paper provides a brief introduction to forensic statistics and describes how chemometrics could be integrated with its established methods to improve the evaluation of evidence in court.

    The paper describes how statistics and chemometrics can be integrated, by analyzing a previous know forensic data set composed of bacterial communities from fingerprints. The presented strategy can be applied in cases where chemical and biological threat agents have been illegally disposed.

  • 2.
    Alsaadi, Sarah
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Wänström, Linda
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Sjögren, Björn
    Linköping University, Department of Behavioural Sciences and Learning, Education, Teaching and Learning.
    Bjärehed, Marlene
    Linköping University, Department of Behavioural Sciences and Learning, Education, Teaching and Learning.
    Thornberg, Robert
    Linköping University, Department of Behavioural Sciences and Learning, Education, Teaching and Learning. Linköping University, Faculty of Educational Sciences.
    Collective moral disengagement and school bullying: An initial validation study of the Swedish scale version2016Conference paper (Refereed)
  • 3.
    Anderskär, Erika
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Thomasson, Frida
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Inkrementell responsanalys av Scandnavian Airlines medlemmar: Vilka kunder ska väljas vid riktad marknadsföring?2017Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Scandinavian Airlines has a large database containing their Eurobonus members. In order to analyze which customers they should target with direct marketing, such as emails, uplift models have been used. With a binary response variable that indicates whether the customer has bought or not, and a binary dummy variable that indicates if the customer has received the campaign or not conclusions can be drawn about which customers are persuadable. That means that the customers that buy when they receive a campaign and not if they don't are spotted. Analysis have been done with one campaign for Sweden and Scandinavia. The methods that have been used are logistic regression with Lasso and logistic regression with Penalized Net Information Value. The best method for predicting purchases is Lasso regression when comparing with a confusion matrix. The variable that best describes persuadable customers in logistic regression with PNIV is Flown (customers that have own with SAS within the last six months). In Lassoregression the variable that describes a persuadable customer in Sweden is membership level1 (the rst level of membership) and in Scandinavia customers that receive campaigns with delivery code 13 are persuadable, which is a form of dispatch.

  • 4.
    Bartoszek, Krzysztof
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Trait evolution with jumps: illusionary normality2017In: Proceedings of the XXIII National Conference on Applications of Mathematics in Biology and Medicine, 2017, 23-28 p.Conference paper (Refereed)
    Abstract [en]

    Phylogenetic comparative methods for real-valued traits usually make use of stochastic process whose trajectories are continuous.This is despite biological intuition that evolution is rather punctuated thangradual. On the other hand, there has been a number of recent proposals of evolutionarymodels with jump components. However, as we are only beginning to understandthe behaviour of branching Ornstein-Uhlenbeck (OU) processes the asymptoticsof branching  OU processes with jumps is an even greater unknown. In thiswork we build up on a previous study concerning OU with jumps evolution on a pure birth tree.We introduce an extinction component and explore via simulations, its effects on the weak convergence of such a process.We furthermore, also use this work to illustrate the simulation and graphic generation possibilitiesof the mvSLOUCH package.

  • 5.
    Bartoszek, Krzysztof
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences. Uppsala University, Sweden.
    Glemin, Sylvain
    Uppsala University, Sweden; CNRS University of Montpellier IRD EPHE, France.
    Kaj, Ingemar
    Uppsala University, Sweden.
    Lascoux, Martin
    Uppsala University, Sweden.
    Using the Ornstein-Uhlenbeck process to model the evolution of interacting populations2017In: Journal of Theoretical Biology, ISSN 0022-5193, E-ISSN 1095-8541, Vol. 429, 35-45 p.Article in journal (Refereed)
    Abstract [en]

    The Ornstein-Uhlenbeck (OU) process plays a major role in the analysis of the evolution of phenotypic traits along phylogenies. The standard OU process includes random perturbations and stabilizing selection and assumes that species evolve independently. However, evolving species may interact through various ecological process and also exchange genes especially in plants. This is particularly true if we want to study phenotypic evolution among diverging populations within species. In this work we present a straightforward statistical approach with analytical solutions that allows for the inclusion of adaptation and migration in a common phylogenetic framework, which can also be useful for studying local adaptation among populations within the same species. We furthermore present a detailed simulation study that clearly indicates the adverse effects of ignoring migration. Similarity between species due to migration could be misinterpreted as very strong convergent evolution without proper correction for these additional dependencies. Finally, we show that our model can be interpreted in terms of ecological interactions between species, providing a general framework for the evolution of traits between "interacting" species or populations.(C) 2017 Elsevier Ltd. All rights reserved.

  • 6.
    Bergstrand, Frida
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Nguyen, Ngan
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Bakgrundsvariablers påverkan på enkätsvaren i en telefonintervju: En studie om effekt av intervjuarens, respondentens och intervjuns egenskaper2017Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Norstat recurrently performs a survey that contains questions about how much the respondent is watching different tv-channels, how different media-devices are used, the ownership of different devices and the usage of different tv-channel sites on the internet, social media, internet services, magazine services and streaming services. In this thesis, data from the survey performed during the autumn of 2016 was used. The aim of this thesis is to examine if there is a difference in answers based on different characteristics of the interviewers and respondents. 

    The 15 most important questions from the survey were chosen in this thesis, and to further reduce the number of response variables principal component analysis was used. The new scores that were produced by the analysis were the reduced response variables, which kept the most important information from the questions in the survey. Thereafter multilevel analyses and regression analyses were performed to examine the effects.  

    The results showed that there was an effect of different characteristics in different questions in the survey. The characteristics that showed effect were the age of the interviewer, the length of the employment, the age of the respondent, education, sex and native language. Some of the questions also showed effect based on whether the respondent lived in a metropolitan region or not.

  • 7.
    Bjärehed, Marlene
    et al.
    Linköping University, Department of Behavioural Sciences and Learning, Education, Teaching and Learning.
    Sjögren, Björn
    Linköping University, Department of Behavioural Sciences and Learning, Education, Teaching and Learning.
    Wänström, Linda
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Thornberg, Robert
    Linköping University, Department of Behavioural Sciences and Learning, Education, Teaching and Learning. Linköping University, Faculty of Educational Sciences.
    Bullying and moral disengagement mechanisms2016Conference paper (Refereed)
  • 8.
    Bjärehed, Marlene
    et al.
    Linköping University, Faculty of Educational Sciences. Linköping University, Department of Behavioural Sciences and Learning, Education, Teaching and Learning.
    Thornberg, Robert
    Linköping University, Department of Behavioural Sciences and Learning, Education, Teaching and Learning. Linköping University, Faculty of Educational Sciences.
    Wänström, Linda
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Gianluca, Gini
    University of Padova.
    Sjögren, Björn
    Linköping University, Faculty of Educational Sciences. Linköping University, Department of Behavioural Sciences and Learning, Education, Teaching and Learning.
    Bullying perpetration and victimization and their associations with warm student–teacher relationship, individual and collective moral disengagement, and collective efficacy in a sample of Swedish fourth grade students: A multi-level analysis2017Conference paper (Refereed)
  • 9.
    Bonneau, Maxime
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Reinforcement Learning for 5G Handover2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The development of the 5G network is in progress, and one part of the process that needs to be optimised is the handover. This operation, consisting of changing the base station (BS) providing data to a user equipment (UE), needs to be efficient enough to be a seamless operation. From the BS point of view, this operation should be as economical as possible, while satisfying the UE needs.  In this thesis, the problem of 5G handover has been addressed, and the chosen tool to solve this problem is reinforcement learning. A review of the different methods proposed by reinforcement learning led to the restricted field of model-free, off-policy methods, more specifically the Q-Learning algorithm. On its basic form, and used with simulated data, this method allows to get information on which kind of reward and which kinds of action-space and state-space produce good results. However, despite working on some restricted datasets, this algorithm does not scale well due to lengthy computation times. It means that the agent trained can not use a lot of data for its learning process, and both state-space and action-space can not be extended a lot, restricting the use of the basic Q-Learning algorithm to discrete variables. Since the strength of the signal (RSRP), which is of high interest to match the UE needs, is a continuous variable, a continuous form of the Q-learning needs to be used. A function approximation method is then investigated, namely artificial neural networks. In addition to the lengthy computational time, the results obtained are not convincing yet. Thus, despite some interesting results obtained from the basic form of the Q-Learning algorithm, the extension to the continuous case has not been successful. Moreover, the computation times make the use of reinforcement learning applicable in our domain only for really powerful computers.

  • 10.
    Brouwers, Jack
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Thellman, Björn
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Klassificering av vinkvalitet2017Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The data used in this paper is an open source data, that was collected in Portugal over a three year period between 2004 and 2007. It consists of the physiochemical parameters, and the quality grade of the wines.

    This study focuses on assessing which variables that primarily affect the quality of a wine and how the effects of the variables interact with each other, and also compare which of the different classification methods work the best and have the highest degree of accuracy.

    The data is divided into red and white wine where the response variable is ordered and consists of the grades of quality for the different wines. Due to the distribution in the response variable having too few observations in some of the quality grades, a new response variable was created where several grades were pooled together so that each different grade category would have a good amount of observations.

    The statistical methods used are Bayesian ordered logistic regression as well as two data mining techniques which are neural networks and decision trees.

    The result obtained showed that for the two types of wine it is primarily the alcohol content and the amount of volatile acid that are recurring parameters which have a great influence on predicting the quality of the wines.

    The results also showed that among the three different methods, decision trees were the best at classifying the white wines and the neural network were the best for the red wines.

  • 11.
    Bruzzone, Andrea
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    P-SGLD: Stochastic Gradient Langevin Dynamics with control variates2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Year after years, the amount of data that we continuously generate is increasing. When this situation started the main challenge was to find a way to store the huge quantity of information. Nowadays, with the increasing availability of storage facilities, this problem is solved but it gives us a new issue to deal with: find tools that allow us to learn from this large data sets. In this thesis, a framework for Bayesian learning with the ability to scale to large data sets is studied. We present the Stochastic Gradient Langevin Dynamics (SGLD) framework and show that in some cases its approximation of the posterior distribution is quite poor. A reason for this can be that SGLD estimates the gradient of the log-likelihood with a high variability due to naïve sampling. Our approach combines accurate proxies for the gradient of the log-likelihood with SGLD. We show that it produces better results in terms of convergence to the correct posterior distribution than the standard SGLD, since accurate proxies dramatically reduce the variance of the gradient estimator. Moreover, we demonstrate that this approach is more efficient than the standard Markov Chain Monte Carlo (MCMC) method and that it exceeds other techniques of variance reduction proposed in the literature such as SAGA-LD algorithm. This approach also uses control variates to improve SGLD so that it is straightforward the comparison with our approach. We apply the method to the Logistic Regression model. 

  • 12.
    Cros, Olivier
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Aalborg Unversity Hospital, Denmark.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Gaihede, Michael
    Department of Otolaryngology, Head & Neck Surgery, Aalborg University Hospital, Denmark.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Enhancement of micro-channels within the human mastoid bone based on local structure tensor analysis2016In: Image Proceessing Theory, Tools and Apllications, IEEE, 2016Conference paper (Refereed)
    Abstract [en]

    Numerous micro-channels have recently been discovered in the human temporal bone by x-ray micro-CT-scanning. After a preliminary study suggesting that these micro-channels form a separate blood supply for the mucosa of the mastoid air cells, a structural analysis of the micro-channels using a local structure tensor was carried out. Despite the high-resolution of the micro-CT scan, presence of noise within the air cells along with missing information in some micro-channels suggested the need of image enhancement. This paper proposes an adaptive enhancement of the micro-channels based on a local structure analysis while minimizing the impact of noise on the overall data. Comparison with an anisotropic diffusion PDE based scheme was also performed.

  • 13.
    Cros, Olivier
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Department of Otolaryngology, Head & Neck Surgery, Aalborg University Hospital, Denmark.
    Gaihede, Michael
    Department of Otolaryngology, Head & Neck Surgery, Aalborg University Hospital, Denmark; Department of Clinical Medicine, Aalborg University, Denmark.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Surface and curve skeleton from a structure tensor analysis applied on mastoid air cells in human temporal bones2017In: IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, 270-274 p.Conference paper (Refereed)
    Abstract [en]

    The mastoid of human temporal bone contains numerous air cells connected to each others. In order to gain further knowledge about these air cells, a more compact representation is needed to obtain an estimate of the size distribution of these cells. Already existing skeletonization methods often fail in producing a faithful skeleton mostly due to noise hampering the binary representation of the data. This paper proposes a different approach by extracting geometrical information embedded in the Euclidean distance transform of a volume via a structure tensor analysis based on quadrature filters, from which a secondary structure tensor allows the extraction of surface skeleton along with a curve skeleton from its eigenvalues. Preliminary results obtained on a X-ray micro-CT scans of a human temporal bone show very promising results.

  • 14.
    Eamrurksiri, Araya
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Applying Machine Learning to LTE/5G Performance Trend Analysis2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The core idea of this thesis is to reduce the workload of manual inspection when the performance analysis of an updated software is required. The Central Process- ing Unit (CPU) utilization, which is one of the essential factors for evaluating the performance, is analyzed. The purpose of this work is to apply machine learning techniques that are suitable for detecting the state of the CPU utilization and any changes in the test environment that affects the CPU utilization. The detection re- lies on a Markov switching model to identify structural changes, which are assumed to follow an unobserved Markov chain, in the time series data. A historical behav- ior of the data can be described by a first-order autoregression. Then, the Markov switching model becomes a Markov switching autoregressive model. Another ap- proach based on a non-parametric analysis, a distribution-free method that requires fewer assumptions, called an E-divisive method, is proposed. This method uses a hi- erarchical clustering algorithm to detect multiple change point locations in the time series data. As the data used in this analysis does not contain any ground truth, the evaluation of the methods is analyzed by generating simulated datasets with known states. Besides, these simulated datasets are used for studying and compar- ing between the Markov switching autoregressive model and the E-divisive method. Results show that the former method is preferable because of its better performance in detecting changes. Some information about the state of the CPU utilization are also obtained from performing the Markov switching model. The E-divisive method is proved to have less power in detecting changes and has a higher rate of missed detections. The results from applying the Markov switching autoregressive model to the real data are presented with interpretations and discussions. 

  • 15.
    Enoksson, Josefin
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Olausson, Sofia
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Bayesiansk flernivåanalys för att undersöka variationen i elevers trygghet i skolan: En studie baserad på enkäten Om mig2017Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
  • 16.
    Gu, Xuan
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Repeated Tractography of a Single Subject: How High Is the Variance?2017In: Modeling, Analysis, and Visualization of Anisotropy / [ed] Thomas Schultz, Evren Özarslan, Ingrid Hotz, Springer Link , 2017, 331-354 p.Chapter in book (Other academic)
    Abstract [en]

    We have investigated the test-retest reliability of diffusion tractography, using 32 diffusion datasets from a single healthy subject. Preprocessing was carried out using functions in FSL (FMRIB Software Library), and tractography was carried out using FSL and Dipy. The tractography was performed in diffusion space, using two seed masks (corticospinal and cingulum gyrus tracts) created from the JHU White-Matter Tractography atlas. The tractography results were then warped into MNI standard space by a linear transformation. The reproducibility of tract metrics was examined using the standard deviation, the coefficient of variation (CV) and the Dice similarity coefficient (DSC), which all indicated a high reproducibility. Our results show that the multi-fiber model in FSL is able to reveal more connections between brain areas, compared to the single fiber model, and that distortion correction increases the reproducibility.

    The full text will be freely available from 2018-07-08 00:00
  • 17.
    Gu, Xuan
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Sidén, Per
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
    Wegmann, Bertil
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Villani, Mattias
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Bayesian Diffusion Tensor Estimation with Spatial Priors2017In: CAIP 2017: Computer Analysis of Images and Patterns, 2017Conference paper (Refereed)
    Abstract [en]

    Spatial regularization is a technique that exploits the dependence between nearby regions to locally pool data, with the effect of reducing noise and implicitly smoothing the data. Most of the currently proposed methods are focused on minimizing a cost function, during which the regularization parameter must be tuned in order to find the optimal solution. We propose a fast Markov chain Monte Carlo (MCMC) method for diffusion tensor estimation, for both 2D and 3D priors data. The regularization parameter is jointly with the tensor using MCMC. We compare FA (fractional anisotropy) maps for various b-values using three diffusion tensor estimation methods: least-squares and MCMC with and without spatial priors. Coefficient of variation (CV) is calculated to measure the uncertainty of the FA maps calculated from the MCMC samples, and our results show that the MCMC algorithm with spatial priors provides a denoising effect and reduces the uncertainty of the MCMC samples.

    The full text will be freely available from 2018-07-29 15:19
  • 18.
    Jesperson, Sara
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Johansson, Sara
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Mönster som leder till sjukfrånvaro: Sekvensanalys på longitudinella data2017Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Absence due to sickness results in a cost to both employers and employees. For an unnamed wholesaler this is a problem at one of their warehouses, where the rate of sick leave is high. The aim of this thesis is to identify interesting patterns over time that lead to sick leave by analyzing data from the company's payroll system and their attendance system.

    The data is longitudinal and to detect the patterns that lead to sick leave, sequence analysis is used. To generate the sequential patterns the algorithm cSPADE is used since it allows time constraints to be specified for the sequences. The relevance of the generated sequences is evaluated with three interest measures: support, confidence and lift.

    Three separate analyses are performed where different variables are used, depending on whether they change over time or have a constant value, and for these analyses the data is aggregated weekly. The most common events that lead to sick leave for the employees are different duration of employment, gender and birth year. A few days sick leave during a week, namely between 8 and 40 hours, is more common among the employees compared to shorter and longer sick leave. It can be noted that the pattern of previous sick leave usually leads to continued sick leave.

    The thesis also highlights the problems that arise in sequence analysis, for example that the constant variables overshadow the non-constant variables in the resulting sequences. This happens when variables that change over time are used in combination with variables that have a constant value, which may occur in longitudinal data.

  • 19.
    Neville, Kevin
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Channel attribution modelling using clickstream data from an online store2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In marketing, behaviour of users is analysed in order to discover which channels (for instance TV, Social media etc.) are important for increasing the user’s intention to buy a product. The search for better channel attribution models than the common last-click model is of major concern for the industry of marketing. In this thesis, a probabilistic model for channel attribution has been developed, and this model is demonstrated to be more data-driven than the conventional last- click model. The modelling includes an attempt to include the time aspect in the modelling which have not been done in previous research. Our model is based on studying different sequence length and computing conditional probabilities of conversion by using logistic regression models. A clickstream dataset from an online store was analysed using the proposed model. This thesis has revealed proof of that the last-click model is not optimal for conducting these kinds of analyses. 

  • 20.
    Pena, Jose M
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
    Representing independence models with elementary triplets2017In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 88, 587-601 p.Article in journal (Refereed)
    Abstract [en]

    In an independence model, the triplets that represent conditional independences between singletons are called elementary. It is known that the elementary triplets represent the independence model unambiguously under some conditions. In this paper, we show how this representation helps performing some operations with independence models, such as finding the dominant triplets or a minimal independence map of an independence model, or computing the union or intersection of a pair of independence models, or performing causal reasoning. For the latter, we rephrase in terms of conditional independences some of Pearls results for computing causal effects. (C) 2016 Elsevier Inc. All rights reserved.

    The full text will be freely available from 2018-12-16 16:18
  • 21.
    Pena, Jose M
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
    Bendtsen, Marcus
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
    Causal effect identification in acyclic directed mixed graphs and gated models2017In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 90, 56-75 p.Article in journal (Refereed)
    Abstract [en]

    We introduce a new family of graphical models that consists of graphs with possibly directed, undirected and bidirected edges but without directed cycles. We show that these models are suitable for representing causal models with additive error terms. We provide a set of sufficient graphical criteria for the identification of arbitrary causal effects when the new models contain directed and undirected edges but no bidirected edge. We also provide a necessary and sufficient graphical criterion for the identification of the causal effect of a single variable on the rest of the variables. Moreover, we develop an exact algorithm for learning the new models from observational and interventional data via answer set programming. Finally, we introduce gated models for causal effect identification, a new family of graphical models that exploits context specific independences to identify additional causal effects. (C) 2017 Elsevier Inc. All rights reserved.

  • 22.
    Quiroz, Matias
    et al.
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Science & Engineering. Research Division, Sveriges Riksbank, Stockholm, Sweden.
    Tran, Minh-Ngoc
    Discipline of Business Analytics, University of Sydney, Camperdown NSW, Australia.
    Villani, Mattias
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
    Kohn, Robert
    Australian School of Business, University of New South Wales, Sydney NSW, Australia.
    Speeding up MCMC by Delayed Acceptance and Data Subsampling2017In: Journal of Computational And Graphical Statistics, ISSN 1061-8600, E-ISSN 1537-2715Article in journal (Refereed)
    Abstract [en]

    The complexity of the Metropolis–Hastings (MH) algorithm arises from the requirement of a likelihood evaluation for the full dataset in each iteration. One solution has been proposed to speed up the algorithm by a delayed acceptance approach where the acceptance decision proceeds in two stages. In the first stage, an estimate of the likelihood based on a random subsample determines if it is likely that the draw will be accepted and, if so, the second stage uses the full data likelihood to decide upon final acceptance. Evaluating the full data likelihood is thus avoided for draws that are unlikely to be accepted. We propose a more precise likelihood estimator that incorporates auxiliary information about the full data likelihood while only operating on a sparse set of the data. We prove that the resulting delayed acceptance MH is more efficient. The caveat of this approach is that the full dataset needs to be evaluated in the second stage. We therefore propose to substitute this evaluation by an estimate and construct a state-dependent approximation thereof to use in the first stage. This results in an algorithm that (i) can use a smaller subsample m by leveraging on recent advances in Pseudo-Marginal MH (PMMH) and (ii) is provably within O(m^-2) of the true posterior.

    The full text will be freely available from 2018-03-24 14:29
  • 23.
    Sandberg, Martina
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Credit Risk Evaluation using Machine Learning2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
  • 24.
    Shipitsyn, Aleksey
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Statistical Learning with Imbalanced Data2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this thesis several sampling methods for Statistical Learning with imbalanced data have been implemented and evaluated with a new metric, imbalanced accuracy. Several modifications and new algorithms have been proposed for intelligent sampling: Border links, Clean Border Undersampling, One-Sided Undersampling Modified, DBSCAN Undersampling, Class Adjusted Jittering, Hierarchical Cluster Based Oversampling, DBSCAN Oversampling, Fitted Distribution Oversampling, Random Linear Combinations Oversampling, Center Repulsion Oversampling.

    A set of requirements on a satisfactory performance metric for imbalanced learning have been formulated and a new metric for evaluating classification performance has been developed accordingly. The new metric is based on a combination of the worst class accuracy and geometric mean.

    In the testing framework nonparametric Friedman's test and post hoc Nemenyi’s test have been used to assess the performance of classifiers, sampling algorithms, combinations of classifiers and sampling algorithms on several data sets. A new approach of detecting algorithms with dominating and dominated performance has been proposed with a new way of visualizing the results in a network.

    From experiments on simulated and several real data sets we conclude that: i) different classifiers are not equally sensitive to sampling algorithms, ii) sampling algorithms have different performance within specific classifiers, iii) oversampling algorithms perform better than undersampling algorithms, iv) Random Oversampling and Random Undersampling outperform many well-known sampling algorithms, v) our proposed algorithms Hierarchical Cluster Based Oversampling, DBSCAN Oversampling with FDO, and Class Adjusted Jittering perform much better than other algorithms, vi) a few good combinations of a classifier and sampling algorithm may boost classification performance, while a few bad combinations may spoil the performance, but the majority of combinations are not significantly different in performance.

  • 25.
    Sjölund, Jens
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Elekta Instrument AB, Kungstensgatan 18, Box 7593, SE-103 93 Stockholm, Sweden.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Özarslan, Evren
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging2017In: IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, 778-782 p.Conference paper (Refereed)
    Abstract [en]

    We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in qspace. By estimating the signal on a grid, we can do synthetic diffusion spectrum imaging: reconstructing the ensemble averaged propagator (EAP) by an inverse Fourier transform. We also propose an alternative reconstruction method guaranteeing a nonnegative EAP that integrates to unity. The reconstruction is validated on data simulated from two Gaussians at various crossing angles. Moreover, we demonstrate on nonuniformly sampled in vivo data that the method is far superior to linear interpolation, and allows a drastic undersampling of the data with only a minor loss of accuracy. We envision the method as a potential replacement for standard diffusion spectrum imaging, in particular when acquistion time is limited.

  • 26.
    Stenquist, Steven
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Hidsjö, Viktor
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Samband mellan elevers motivationer ochåskådarbeteenden vid mobbningssituationer.: En jämförelse av resultat från multilevel- och faktoranalyser2017Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Previous research has shown that children can be divided into different roles during situations ofbullying in primary school. Therefore, it is of interest to find factors that affect what those roles couldbe.

    In this thesis, the purpose is to analyze the relation between motivation (Extrinsic, Amotivation,Identification) and behavior (Defending, Passive, Pro-Bullying). It is also of interest to analyzepossible differences in behavior depending on age and gender. To be able to analyze relationsbetween psychological concepts, latent variables will be used in conjunction with models for factoranalysis (SEM). This type of model does not take differences between classes into consideration,therefore multilevel models will also be used, where the latter takes differences between classes intoconsideration but not the fact that the variables are latent.

    Another aspect of this thesis will be to compare the methods to each other and analyze potentialdifferences in drawn conclusions. There will be two different types of multilevel models, one ofwhich uses mean as the estimator for the latent variable while the other model will be using factorscores that are calculated with a confirmative factor analysis.

    Results showed that there are significant relations between all types of behavior and motivations inat least one of the methods used. In all the methods, there were significant positive relationsbetween Passive and Amotivation, Defending and Identification, as well as Pro-Bullying and Extrinsic.

    Relations that had a significant negative association were Passive and Identification, Pro-Bullying andIdentification, as well as Defending and Amotivation.

    Relations that were significant, but only in one method, are the following: Pro-Bullying andAmotivation, Passive and Extrinsic, Defending and Extrinsic. These have a positive relation.

    There is no relation between gender, or age, and a specific behavior that was significant in all themethods used. Relation between behavior and age, or gender, that was significant in at least one, isPassive and gender, Defending and gender, Defending and age. The results from this show that boysare less passive and more defending compared to girls. The relation between Defending and age isnegative.

    The intraclass correlation, and Likelihood ratio test, shows that there is a significant variancebetween classes in the three behaviors when a model without explanatory variables is being used.The tests also indicated that there was no significant variance in behavior between schools. ALikelihood ratio test of models with explanatory variables show that the only type of behavior with asignificant difference between students and classes is Defending.

    The difference between factor scores and mean was very small, there were only differences in therelation between Defending and age. Between SEM and Multilevel, the difference is greater, wherethere are differences in conclusion in five of the relations; Passive and extrinsic, Pro-Bullying andAmotivation, Passive and gender, Defending and Extrinsic, as well as Defending and gender.

  • 27.
    Sternelöv, Gustav
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Analysis of forklift data – A process for decimating data and analyzing fork positioning functions2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Investigated in this thesis are the possibilities and effects of reducing CAN data collected from forklifts. The purpose of reducing the data was to create the possibility of exporting and managing data for multiple forklifts and a relatively long period of time. For doing that was an autoregressive filter implemented for filtering and decimating data. Connected to the decimation was also the aim of generating a data set that could be used for analyzing lift sequences and in particular the usage of fork adjustment functions during lift sequences.

    The findings in the report are that an AR (18) model works well for filtering and decimating the data. Information losses are unavoidable but kept at a relatively low level, and the size of data becomes manageable. Each row in the decimated data is labeled as belonging to a lift sequence or as not belonging to a lift sequence given a manually specified definition of the lift sequence event. From the lift sequences is information about the lift like number of usages of each fork adjustment function, load weight and fork height gathered. The analysis of the lift sequences gave that the lift/lower function on average is used 4.75 times per lift sequence and the reach function 3.23 times on average. For the side shift the mean is 0.35 per lift sequence and for the tilt the mean is 0.10. Moreover, it was also found that the struggling time on average is about 17 % of the total lift sequence time. The proportion of the lift that is struggling time was also shown to differ between drivers, with the lowest mean proportion being 7 % and the highest 30 %. 

  • 28.
    Svahn, Caroline
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Automated Bug Report Routing2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    As the software industry grows larger by the minute, the need for automated solutions within bug report management is on the rise. Although some research has been conducted in the area of bug handling, new, faster or more precise approaches are yet to be developed. A bug report typically contains a free text observations field where the issue can be described by a human. Research regarding processing of this type of field is extensive, however, bug reports are often accompanied with system log files which have been given less attention so far. In the 4G LTE telecommunications network, the available system log files are many and several are likely to aid the routing of bug reports. In this thesis, one system log file was chosen to be evaluated; the alarm log. The alarm logs are time series count data containing alarms raised by the system. The alarm log data have been pre-processed with data mining techniques. The Apriori algorithm has been used to mine for specific alarms and alarming objects which indicates that the bug report should be solved by a particular developer group. We extend the Apriori algorithm to a temporal setting by using a customised time dependent confidence measure. To further mine for interesting sequences of events in the logs, the sequence mining approach SPADE has been used. The extracted class-associated sequences from both pre-processing approaches are transformed into binary features possible to use as predictors in any prediction model.

    The results have been evaluated by predicting the correct developer group with two different methods; logistic regression and DO-probit. Logistic regression was regularised with the elastic net penalty to avoid computational issues as well as handling the sparse covariate set. DO-probit was used with a horseshoe prior; it is well suited for the sparse covariate regression problem as it is customised to obtain signals in sparse, noisy data. The results indicate that a data mining approach for processing alarm logs is promising.

    The results show that the rules obtained with the Apriori mining process are suitable for mining the alarm logs as most binary representations of the rules used as covariates in logistic regression are kept in the equations for the expected classes with strongly positive coefficients. Although, the overall improvement in accuracy from using the alarms logs in addition to the learned topics from free text fields is modest, the alarm logs are concluded to be a good complement to the free text information as some Apriori covariates appears to be better suited to predict some classes than some topics.

  • 29.
    Thornberg, Robert
    et al.
    Linköping University, Department of Behavioural Sciences and Learning, Education, Teaching and Learning. Linköping University, Faculty of Educational Sciences.
    Wänström, Linda
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    School bullying and victimization and their associations with classroom prevalence of bystander responses, individual tendency of blaming the victim, and gender: A multi-level analysis2017Conference paper (Refereed)
  • 30.
    Thornberg, Robert
    et al.
    Linköping University, Department of Behavioural Sciences and Learning, Education, Teaching and Learning. Linköping University, Faculty of Educational Sciences.
    Wänström, Linda
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Hong, Jun Sung
    Wayne State University, USA; Sungkyunkwan University, South Korea.
    Espelage, Dorothy
    University of Florida, USA.
    Classroom Relationship Qualities and Social-Cognitive Correlates of Defending and Passive Bystanding in School Bullying in Sweden: A Multilevel Analysis2017In: Journal of School Psychology, ISSN 0022-4405, E-ISSN 1873-3506, ISSN 0022-4405, Vol. 63, 49-62 p.Article in journal (Refereed)
    Abstract [en]

    Using the social-ecological and social cognitive theories as integrated guiding frameworks, the present study examined whether moral disengagement and defender self-efficacy at the individual level, and moral disengagement, quality of teacher–student relationships and quality of student–student relationships at the classroom level were associated with passive bystanding and defending in bullying situations. Participants were 900 Swedish students from 43 classrooms, ranging in age from 9 to 13 years. Multilevel regression analyses revealed that passive reactions by bystanders were associated with greater moral disengagement and less defender self-efficacy. Defending, in turn, was associated with less moral disengagement and greater defender self-efficacy and classroom student–student relationship quality. Furthermore, students who scored high in moral disengagement were even less prone to defend victims when the classroom student–student relationship quality was low, but more prone to act as defenders when the classroom student–student relationship quality was high. In addition, the negative association between defender self-efficacy and passive bystanding was stronger both in classrooms with higher student–student relationship quality and in those with lower class moral disengagement. Implications for prevention are discussed.

  • 31.
    Thornberg, Robert
    et al.
    Linköping University, Department of Behavioural Sciences and Learning, Education, Teaching and Learning. Linköping University, Faculty of Educational Sciences.
    Wänström, Linda
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Hong, Jun Sung
    Wayne State University.
    Espelage, Dorothy
    University of Florida.
    Individual and class socio-moral influences on how to act as a bystander in school bullying situations2016Conference paper (Refereed)
  • 32.
    Tran, Vuong
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Bayesian variable selection in linear mixed effects models2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
  • 33.
    Villani, Mattias
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University.
    Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models2017In: Journal of Computational And Graphical Statistics, ISSN 1061-8600, E-ISSN 1537-2715Article in journal (Refereed)
  • 34.
    Wegmann, Bertil
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Villani, Mattias
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Bayesian Heteroscedastic Regression for Diffusion Tensor Imaging2017In: Modeling, Analysis, and Visualization of Anisotropy / [ed] Thomas Schultz, Evren Özarslan and Ingrid Hotz, Springer Publishing Company, 2017, 1, 257-282 p.Chapter in book (Refereed)
    Abstract [en]

    We propose a single-diffusion tensor model with heteroscedastic noise and a Bayesian approach via a highly efficient Markov Chain Monte Carlo (MCMC) algorithm for inference. The model is very flexible since both the noise-free signal and the noise variance are functions of diffusion covariates, and the relevant covariates in the noise are automatically selected by Bayesian variable selection. We compare the estimated diffusion tensors from our model to a homoscedastic counterpart with no covariates in the noise, and to commonly used linear and nonlinear least squares methods. The estimated single-diffusion tensors within each voxel are compared with respect to fractional anisotropy (FA) and mean diffusivity (MD). Using data from the Human Connectome Project, our results show that the noise is clearly heteroscedastic, especially the posterior variance for MD is substantially underestimated by the homoscedastic model, and inferences from the homoscedastic model are on average spuriously precise. Inferences from commonly used ordinary and weighted least squares methods (OLS and WLS) show that it is not adequate to estimate the single-diffusion tensor from logarithmic measurements.

  • 35.
    Wegmann, Bertil
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Faculty of Arts and Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Villani, Mattias
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Bayesian Rician Regression for Neuroimaging2017In: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 11, 586Article in journal (Refereed)
    Abstract [en]

    It is well-known that data from diffusion weighted imaging (DWI) follow the Rician distribution. The Rician distribution is also relevant for functional magnetic resonance imaging (fMRI) data obtained at high temporal or spatial resolution. We propose a general regression model for non-central chi (NC-chi) distributed data, with the heteroscedastic Rician regression model as a prominent special case. The model allows both parameters in the Rician distribution to be linked to explanatory variables, with the relevant variables chosen by Bayesian variable selection. A highly efficient Markov chain Monte Carlo (MCMC) algorithm is proposed to capture full model uncertainty by simulating from the joint posterior distribution of all model parameters and the binary variable selection indicators. Simulated regression data is used to demonstrate that the Rician model is able to detect the signal much more accurately than the traditionally used Gaussian model at low signal-to-noise ratios. Using a diffusion dataset from the Human Connectome Project, it is also shown that the commonly used approximate Gaussian noise model underestimates the mean diffusivity (MD) and the fractional anisotropy (FA) in the single-diffusion tensor model compared to the Rician model.

  • 36.
    Wilhelmsson, Rasmus
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Engarås, Anton
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    En utvärdering av reliabilitet och mätinvarians hos ett självtest för spelberoende2017Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [sv]

    GamTest är ett självtest för tidiga indikationer på spelberoende som är utvecklat av Sustainable Interaction. Testet består av 15 frågor över 5 olika problemdimensioner, denna uppsats fokuserar på att förkorta detta test samt undersöka skillnader mellan exempelvis kön och ålder.

    Statistiska metoder som används är Cronbachs alfa, konfirmativ faktoranalys samt olika typer av multigroup-analyser.

    Resultatet av analyserna visar att antalet frågor i testet kan förkortas till 13 stycken utan att tillförlitligheten förändrades negativt. En modell med 13 frågor visade även på en bättre modellanpassning i den konfirmativa faktoranalysen jämfört med den ursprungliga modellen. Anpassningen förbättras ytterligare med 11 frågor men denna modell har en något lägre reliabilitet. Detta betyder att respondentens tid för testet kan förkortas utan att pålitligheten försämras. Multigroup-analyser visar att spelproblem definieras olika för kön och för ålder.

    Flera frågor stämmer överens med de som finns i ett annat välkänt mätinstrument för spelproblem, Problem Gambling Severity Index, men i GamTest finns frågor som handlar om problem med konsumtion i tid vilket saknas i det förstnämnda. Överlag så är testen annars väldigt lika. 

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