A discussion on hidden Markov models for life course data

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Version: Final published version
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Serval ID
serval:BIB_699F53CEEB6C
Type
A part of a book
Collection
Publications
Institution
Title
A discussion on hidden Markov models for life course data
Title of the book
Proceedings of the International Conference on Sequence Analysis and Related Methods (LaCOSA II)
Author(s)
Bolano Danilo, Berchtold André, Ritschard Gilbert
Publisher
NCCR LIVES
Address of publication
Lausanne, Switzerland
Publication state
Published
Issued date
2016
Pages
241-260
Language
english
Notes
2549
Abstract
This is an introduction on discrete-time Hidden Markov models (HMM)
for longitudinal data analysis in population and life course studies. In the Markovian
perspective, life trajectories are considered as the result of a stochastic process
in which the probability of occurrence of a particular state or event depends on the
sequence of states observed so far. Markovian models are used to analyze the transition
process between successive states. Starting from the traditional formulation
of a first-order discrete-time Markov chain where each state is liked to the next
one, we present the hidden Markov models where the current response is driven
by a latent variable that follows a Markov process. The paper presents also a simple
way of handling categorical covariates to capture the effect of external factors
on the transition probabilities and existing software are briefly overviewed. Empirical
illustrations using data on self reported health demonstrate the relevance of the
different extensions for life course analysis.
Keywords
Sequence Analysis, Life course approach, Hidden Markov Model
Create date
18/08/2016 20:17
Last modification date
24/01/2020 8:09
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