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Latent transition analysis: Inference and estimation
Author(s) -
Chung Hwan,
Lanza Stephanie T.,
Loken Eric
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3130
Subject(s) - markov chain monte carlo , inference , computer science , bayesian inference , bayesian probability , context (archaeology) , latent variable , markov chain , statistical inference , econometrics , statistics , artificial intelligence , machine learning , mathematics , paleontology , biology
Parameters for latent transition analysis (LTA) are easily estimated by maximum likelihood (ML) or Bayesian method via Markov chain Monte Carlo (MCMC). However, unusual features in the likelihood can cause difficulties in ML and Bayesian inference and estimation, especially with small samples. In this study we explore several problems in drawing inference for LTA in the context of a simulation study and a substance use example. We argue that when conventional ML and Bayesian estimates behave erratically, problems often may be alleviated with a small amount of prior input for LTA with small samples. This paper proposes a dynamic data‐dependent prior for LTA with small samples and compares the performance of the estimation methods with the proposed prior in drawing inference. Copyright © 2007 John Wiley & Sons, Ltd.