Chung Hwan, Lanza Stephanie T, Loken Eric
Department of Epidemiology, Michigan State University, East Lansing, MI 48824, U.S.A.
Stat Med. 2008 May 20;27(11):1834-54. doi: 10.1002/sim.3130.
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.
潜在转变分析（LTA）的参数可以通过最大似然法（ML）或经由马尔可夫链蒙特卡罗（MCMC）的贝叶斯方法轻松估计。然而，似然函数中的异常特征可能会给最大似然法和贝叶斯推断及估计带来困难，尤其是在样本量较小时。在本研究中，我们在模拟研究和物质使用示例的背景下探讨了在对LTA进行推断时的几个问题。我们认为，当传统的最大似然法和贝叶斯估计表现不稳定时，对于小样本的LTA，少量的先验输入通常可以缓解问题。本文提出了一种针对小样本LTA的动态数据依赖先验，并比较了在进行推断时使用所提出先验的估计方法的性能。
