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Publications (4 of 4) Show all publications
Helske, S. & Helske, J. (2019). Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R. Journal of Statistical Software, 88(3), 1-32
Open this publication in new window or tab >>Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R
2019 (English)In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 88, no 3, p. 32p. 1-32Article in journal (Refereed) Published
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

Place, publisher, year, edition, pages
Alexandria, VA, United States: American Statistical Association, 2019. p. 32
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-154355 (URN)10.18637/jss.v088.i03 (DOI)000457019000001 ()
Available from: 2019-02-07 Created: 2019-02-07 Last updated: 2019-03-07Bibliographically approved
Eerola, M. & Helske, S. (2018). Analysis of Life History Calendar Data. In: N. Balakrishnan, Theodore Colton, Brian Everitt, Walter Piegorsch, Fabrizio Ruggeri and Jozef L. Teugels (Ed.), Wiley StatsRef: Statistics Reference Online: (pp. 1-8). John Wiley & Sons
Open this publication in new window or tab >>Analysis of Life History Calendar Data
2018 (English)In: Wiley StatsRef: Statistics Reference Online / [ed] N. Balakrishnan, Theodore Colton, Brian Everitt, Walter Piegorsch, Fabrizio Ruggeri and Jozef L. Teugels, John Wiley & Sons, 2018, p. 1-8Chapter in book (Other academic)
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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
Series
Major Reference Works
Keywords
life course analysis, life history calendar, multidimensional sequence analysis, multistate model, prediction probability
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-147279 (URN)10.1002/9781118445112.stat08005 (DOI)9781118445112 (ISBN)
Available from: 2018-04-12 Created: 2018-04-12 Last updated: 2018-04-12
Helske, S., Helske, J. & Eerola, M. (2018). Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data. In: Gilbert Ritschard, Matthias Studer (Ed.), Sequence Analysis and Related Approaches: (pp. 185-200). Switzerland: Springer
Open this publication in new window or tab >>Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data
2018 (English)In: Sequence Analysis and Related Approaches / [ed] Gilbert Ritschard, Matthias Studer, Switzerland: Springer, 2018, p. 185-200Chapter in book (Refereed)
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.

Place, publisher, year, edition, pages
Switzerland: Springer, 2018
Series
Life Course Research and Social Policies, ISSN 2211-7776, E-ISSN 2211-7784 ; 10
Keywords
life course, longitudinal data, sequence analysis, family and work trajectories, Markov models, hidden Markov models, latent Markov models, population dynamics
National Category
Probability Theory and Statistics Social Sciences Interdisciplinary
Identifiers
urn:nbn:se:liu:diva-152155 (URN)10.1007/978-3-319-95420-2_11 (DOI)978-3-319-95420-2 (ISBN)978-3-319-95419-6 (ISBN)
Available from: 2018-10-19 Created: 2018-10-19 Last updated: 2018-10-19Bibliographically approved
Helske, S., Steele, F., Kokko, K., Räikkönen, E. & Eerola, M. (2015). Partnership formation and dissolution over the life course: applying sequence analysis and event history analysis in the study of recurrent events. Longitudinal and life course studies, 6(1), 1-25
Open this publication in new window or tab >>Partnership formation and dissolution over the life course: applying sequence analysis and event history analysis in the study of recurrent events
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2015 (English)In: Longitudinal and life course studies, ISSN 1124-9064, E-ISSN 1757-9597, Vol. 6, no 1, p. 1-25Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
London, United Kingdom: Society for Longitudinal and Life Course Studies, 2015
Keywords
partnership formation; partnership dissolution; sequence analysis; event history analysis; repeated events
National Category
Applied Psychology Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-142718 (URN)10.14301/llcs.v6i1.290 (DOI)
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2017-12-04Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0532-0153

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