The latent Markov model and the growth mixture model for longitudinal data are compared when the ordinal nature of the response variable is of interest. The latent Markov model is based on time-varying latent variables to explain the observable behaviour of the individuals. It is proposed in a semiparametric formulation as the latent process has a discrete distribution and is characterized by a Markov structure. The growth mixture model is based on a latent categorical variable that accounts for the unobserved heterogeneity in the observed trajectories and on a mixture of Gaussian random variables to account for the variability in the growth factors. We refer to a real data example on self-reported health status to illustrate their peculiarities and differences

Pennoni, F., Romeo, I. (2017). A comparison between two statistical models to analyse and predict individual changes over time. Intervento presentato a: Scientific Meeting of the FIRB project on “Mixture and Latent Variable Models for Causal Inference and Analysis of Socio- Economic Data”, Bologna, Italia.

A comparison between two statistical models to analyse and predict individual changes over time

PENNONI, FULVIA;
2017

Abstract

The latent Markov model and the growth mixture model for longitudinal data are compared when the ordinal nature of the response variable is of interest. The latent Markov model is based on time-varying latent variables to explain the observable behaviour of the individuals. It is proposed in a semiparametric formulation as the latent process has a discrete distribution and is characterized by a Markov structure. The growth mixture model is based on a latent categorical variable that accounts for the unobserved heterogeneity in the observed trajectories and on a mixture of Gaussian random variables to account for the variability in the growth factors. We refer to a real data example on self-reported health status to illustrate their peculiarities and differences
No
abstract + slide
Expectation-Maximization algorithm, growth curve model,
 latent Markov model, longitudinal ordinal responses, Monte Carlo methods, Viterbi algorithm
English
Scientific Meeting of the FIRB project on “Mixture and Latent Variable Models for Causal Inference and Analysis of Socio- Economic Data”
https://eventi.unibo.it/firb-statistics-bologna-2017
Pennoni, F., Romeo, I. (2017). A comparison between two statistical models to analyse and predict individual changes over time. Intervento presentato a: Scientific Meeting of the FIRB project on “Mixture and Latent Variable Models for Causal Inference and Analysis of Socio- Economic Data”, Bologna, Italia.
Pennoni, F; Romeo, I
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/146962
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