Latent transition analysis: Inference and estimation

Hwan Chung, Stephanie T. Lanza, Eric Loken

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1834-1854
Number of pages21
JournalStatistics in Medicine
Issue number11
StatePublished - May 20 2008

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability


Dive into the research topics of 'Latent transition analysis: Inference and estimation'. Together they form a unique fingerprint.

Cite this