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.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Chung H,Lanza ST,Loken Edoi
10.1002/sim.3130subject
Has Abstractpub_date
2008-05-20 00:00:00pages
1834-54issue
11eissn
0277-6715issn
1097-0258journal_volume
27pub_type
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