A Latent Markov Model Approach to the Estimation of Response Errors in Multiwave Panel Data

Bye, Barry V.; Schechter, Evan S.

doi:10.2307/2289226

Cited by 6 publications

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“…A related approach is the latent transition analysis (Bye and Schechter, 1986;Langeheine, 1988;Collins and Wugalter, 1992;Kaplan, 2008) in which the parameters of the LM models are allowed to vary in different latent subpopulations.…”

Section: Model Motivations and Historical Reviewmentioning

confidence: 99%

Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates

Bartolucci

Farcomeni

Pennoni

2014

TEST

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We provide a comprehensive overview of latent Markov (LM) models for the analysis of longitudinal data. The main assumption behind these models is that the response variables are conditionally independent given a latent process which follows a first-order Markov chain. We first illustrate the more general version of the LM model which includes individual covariates.We then illustrate several constrained versions of the general LM model, which make the model more parsimonious and allow us to consider and test hypotheses of interest. These constraints may be put on the conditional distribution of the response variables given the latent process (measurement model) or on the distribution of the latent process (latent model). For the general version of the model we also illustrate in detail maximum likelihood estimation through the Expectation-Maximization algorithm, which may be efficiently implemented by recursions known in the hidden Markov literature. We discuss about the model identifiability and we outline methods for obtaining standard errors for the parameter estimates. We also illustrate methods for selecting the number of states and for path prediction. Finally, we illustrate Bayesian estimation method. Models and related inference are illustrated by the description of relevant socio-economic applications available in the literature.

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Section: Model Motivations and Historical Reviewmentioning

confidence: 99%

Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates

Bartolucci

Farcomeni

Pennoni

2014

TEST

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“…Poulsen (1982) was the first to introduce a formulation of the mixed Markov chain (MMC) model, but his work was not officially published (see Van de Pol, 1990). Eshima, et al (1984Eshima, et al ( , 1985, Bye & Schechter (1986) and Van de Pol & De Leeuw (1986) also treated similar topics. Moreover Van de Pol (1990) discussed LMMC model as an extended version of LMC model.…”

Section: Introductionmentioning

confidence: 97%

Dynamic Latent Structure Analysis Through the Latent Markov Chain Model

Eshima

1993

Behaviormetrika

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“…(As a convention the term``marginal'' refers to the quantities which are de®ned on the observed data.) Such latent structures arise from all incomplete-data situations of which we only mention sample surveys (Lazarsfeld & Henry, 1968;Wiggins, 1973;Clogg & Goodman, 1984;Bye & Schechter, 1986;Bjùrnstad & Sommervoll, 1995).…”

Section: Introductionmentioning

confidence: 99%

Likelihood Imputation

Zhang

1998

Scandinavian J Statistics

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The method of likelihood imputation is devised under the framework of latent structure models where the observation is a statistic of the complete data which can only be speci®ed on a latent basis. The imputed data set is chosen to differ least from the observed one in their information contentsÐa concept with general implications for the analysis of incomplete-data. In contrast to the standard conditional-mean single imputation, our procedure depends on an entire likelihood region instead of any single point in it, and yields consistent parameter estimators nevertheless. We explain its implementations and illustrate with data from panel surveys and linear regression with censorship. We also discuss its potentials in sensitivity analysis.

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A Latent Markov Model Approach to the Estimation of Response Errors in Multiwave Panel Data

Cited by 6 publications

References 0 publications

Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates

Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates

Dynamic Latent Structure Analysis Through the Latent Markov Chain Model

Likelihood Imputation

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