Latent transition analysis: Inference and estimation

Citation

Chung, H.; Lanza, S.; T; & Loken, E. (2008). Latent transition analysis: Inference and estimation. Statistics in Medicine. vol. 27 pp. 1834-1854

Abstract

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.

URL

http://onlinelibrary.wiley.com/doi/10.1002/sim.3130/pdf

Reference Type

Journal Article

Journal Title

Statistics in Medicine

Author(s)

Chung, H.
Lanza, S.
T
Loken, E.

Year Published

2008

Volume Number

27

Pages

1834-1854

DOI

10.1002/sim.3130

Reference ID

8706