Latent Markov (LM) models represent an important class of models for the analysis of longitudinal data, especially when response variables are categorical. These models have a great potential of application in many fields, such as economics and medicine. We illustrate the R package LMest that is tailored to deal with the basic LM model and some extended formulations accounting for individual covariates and for the presence of unobserved clusters of units having the same initial and transition probabilities (mixed LM model). The main functions of the package are tailored to parameter estimation through the expectation-maximization algorithm, which is based on suitable forwardbackward recursions. The package also permits local and global decoding and to obtain standard errors for the parameter estimates. We illustrate the use of the package and its main features through some empirical examples in the fields of labour market, health, and criminology.

Bartolucci, F., Pandolfi, S., Pennoni, F. (2017). Lmest: An R package for latent Markov models for longitudinal categorical data. JOURNAL OF STATISTICAL SOFTWARE, 81(4), 1-38 [10.18637/jss.v081.i04].

Lmest: An R package for latent Markov models for longitudinal categorical data

PENNONI, FULVIA
Ultimo
2017

Abstract

Latent Markov (LM) models represent an important class of models for the analysis of longitudinal data, especially when response variables are categorical. These models have a great potential of application in many fields, such as economics and medicine. We illustrate the R package LMest that is tailored to deal with the basic LM model and some extended formulations accounting for individual covariates and for the presence of unobserved clusters of units having the same initial and transition probabilities (mixed LM model). The main functions of the package are tailored to parameter estimation through the expectation-maximization algorithm, which is based on suitable forwardbackward recursions. The package also permits local and global decoding and to obtain standard errors for the parameter estimates. We illustrate the use of the package and its main features through some empirical examples in the fields of labour market, health, and criminology.
Articolo in rivista - Articolo scientifico
Expectation-maximization algorithm; Forward-backward recursions; Hidden Markov model; Missing data;
Expectation-Maximization algorithm, Forward-backward recursions, Hidden Markov model, Missing data
English
2017
81
4
1
38
partially_open
Bartolucci, F., Pandolfi, S., Pennoni, F. (2017). Lmest: An R package for latent Markov models for longitudinal categorical data. JOURNAL OF STATISTICAL SOFTWARE, 81(4), 1-38 [10.18637/jss.v081.i04].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/172286
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