Pennoni, Fulvia and Romeo, Isabella (2016): Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison.

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Abstract
We propose a short review between two alternative ways of modeling stability and change of longitudinal data when timefixed and timevarying covariates referred to the observed individuals are available. They both build on the foundation of the finite mixture models and are commonly applied in many fields. They look at the data by a different perspective and in the literature they have not been compared when the ordinal nature of the response variable is of interest. The latent Markov model is based on timevarying latent variables to explain the observable behavior of the individuals. The model is proposed in a semiparametric formulation as the latent Markov process has a discrete distribution and it 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 normally distributed random variable to account for the variability of growth rates. To illustrate the main differences among them we refer to a real data example on the self reported health status.
Item Type:  MPRA Paper 

Original Title:  Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison 
English Title:  Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison 
Language:  English 
Keywords:  Dynamic factor model, ExpectationMaximization algorithm, ForwardBackward recursions, Latent trajectories, Maximum likelihood, Monte Carlo methods. 
Subjects:  C  Mathematical and Quantitative Methods > C0  General > C02  Mathematical Methods C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C18  Methodological Issues: General C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C33  Panel Data Models ; Spatiotemporal Models C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C38  Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling I  Health, Education, and Welfare > I1  Health > I11  Analysis of Health Care Markets I  Health, Education, and Welfare > I1  Health > I12  Health Behavior 
Item ID:  72939 
Depositing User:  Prof. Fulvia Pennoni 
Date Deposited:  11 Aug 2016 10:19 
Last Modified:  27 Sep 2019 09:19 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/72939 