Goodness-of-Fit Tests in Two-State Processes
针对医学和社会科学中常见的二元依赖数据，提出两状态马尔可夫链模型的拟合优度检验方法，并讨论将首观测值赋予均衡分布对面板数据效率的提升。
Binary data in which successive outcomes may be dependent frequently arise in a medical and social sciences context. A simple model for such data is a homogeneous 2-state Markov chain. Under this model one would usually treat the first observation in each sequence, Y1, as fixed. In the case of panel data this may constitute a substantial loss of information. In this paper we propose goodness-of-fit tests for the 2-state Markov model under several likely alternative hypotheses of interest. We also discuss the possibility of assigning to Y1 the equilibrium distribution of the chain and outline the gains in efficiency that this can achieve for panel data.