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  • 1.
    Eerola, Mervi
    et al.
    University of Turku, Finland.
    Helske, Satu
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten.
    Analysis of Life History Calendar Data2018Ingår i: Wiley StatsRef: Statistics Reference Online / [ed] N. Balakrishnan, Theodore Colton, Brian Everitt, Walter Piegorsch, Fabrizio Ruggeri and Jozef L. Teugels, John Wiley & Sons, 2018, s. 1-8Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    The life history calendar (LHC) is a data‐collection tool for obtaining reliable retrospective data on several life domains. LHC data can be analyzed either with probabilistic modeling of transitions between the life states or with sequence analysis, a data‐mining method that requires minimal simplification of the original data. The life events define the multistate model and its event‐specific hazards and the parallel life domains in multidimensional sequence analysis. These two approaches complement each other, and recently also several ways to combine them have been suggested.

  • 2.
    Helske, Satu
    et al.
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. University of Oxford, United Kingdom; University of Jyväskylä, Finland.
    Helske, Jouni
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska fakulteten. University of Jyväskylä, Finland.
    Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R2019Ingår i: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 88, nr 3, s. 32s. 1-32Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to describe, visualize, and compare large sequence data, especially when there are multiple parallel sequences per subject. Hidden (latent) Markov models (HMMs) are able to detect underlying latent structures and they can be used in various longitudinal settings: to account for measurement error, to detect unobservable states, or to compress information across several types of observations. Extending to mixture hidden Markov models (MHMMs) allows clustering data into homogeneous subsets, with or without external covariates. The seqHMM package in R is designed for the efficient modeling of sequences and other categorical time series data containing one or multiple subjects with one or multiple interdependent sequences using HMMs and MHMMs. Also other restricted variants of the MHMM can be fitted, e.g., latent class models, Markov models, mixture Markov models, or even ordinary multinomial regression models with suitable parameterization of the HMM. Good graphical presentations of data and models are useful during the whole analysis process from the first glimpse at the data to model fitting and presentation of results. The package provides easy options for plotting parallel sequence data, and proposes visualizing HMMs as directed graphs.less thanbr /greater thanComment: 33 pages, 8 figures

  • 3.
    Helske, Satu
    et al.
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Department of Sociology, University of Oxford, Oxford, UK / Department of Mathematics and Statistics, University of Jyvaskyla, Jyvaskyla, Finland.
    Helske, Jouni
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska fakulteten. Department of Mathematics and Statistics, University of Jyvaskyla, Jyvaskyla, Finland.
    Eerola, Mervi
    Centre of Statistics, University of Turku, Turku, Finland.
    Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data2018Ingår i: Sequence Analysis and Related Approaches / [ed] Gilbert Ritschard, Matthias Studer, Switzerland: Springer, 2018, s. 185-200Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Life course data often consists of multiple parallel sequences, one for each life domain of interest. Multichannel sequence analysis has been used for computing pairwise dissimilarities and finding clusters in this type of multichannel (or multidimensional) sequence data. Describing and visualizing such data is, however, often challenging. We propose an approach for compressing, interpreting, and visualizing the information within multichannel sequences by finding (1) groups of similar trajectories and (2) similar phases within trajectories belonging to the same group. For these tasks we combine multichannel sequence analysis and hidden Markov modelling. We illustrate this approach with an empirical application to life course data but the proposed approach can be useful in various longitudinal problems.

  • 4.
    Helske, Satu
    et al.
    University of Jyväskylä, Jyväskylän yliopisto, Finland.
    Steele, Fiona
    London School of Economics and Political Science, London, UK.
    Kokko, Katja
    University of Jyväskylä, Jyväskylän yliopisto, Finland.
    Räikkönen, Eija
    University of Jyväskylä, Jyväskylän yliopisto, Finland.
    Eerola, Mervi
    University of Turku, Åbo, Finland.
    Partnership formation and dissolution over the life course: applying sequence analysis and event history analysis in the study of recurrent events2015Ingår i: Longitudinal and life course studies, ISSN 1124-9064, E-ISSN 1757-9597, Vol. 6, nr 1, s. 1-25Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We present two types of approach to the analysis of recurrent events for discretely measured data, and show how these methods can complement each other when analysing co-residential partnership histories. Sequence analysis is a descriptive tool that gives an overall picture of the data and helps to find typical and atypical patterns in histories. Event history analysis is used to make conclusions about the effects of covariates on the timing and duration of the partnerships. As a substantive question, we studied how family background and childhood socio-emotional characteristics were related to later partnership formation and stability in a Finnish cohort born in 1959. We found that high self-control of emotions at age 8 was related to a lower risk of partnership dissolution and for women a lower probability of repartnering. Child-centred parenting practices during childhood were related to a lower risk of dissolution for women. Socially active boys were faster at forming partnerships as men.

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