纵向分类数据的边缘化转换模型与似然推断

Marginalized transition models and likelihood inference for longitudinal categorical data.

作者信息

Heagerty Patrick J

机构信息

Department of Biostatistics, University of Washington, Seattle 98195, USA.

出版信息

Biometrics. 2002 Jun;58(2):342-51. doi: 10.1111/j.0006-341x.2002.00342.x.

DOI:10.1111/j.0006-341x.2002.00342.x

PMID:12071407

Abstract

Marginal generalized linear models are now frequently used for the analysis of longitudinal data. Semiparametric inference for marginal models was introduced by Liang and Zeger (1986, Biometrics 73, 13-22). This article develops a general parametric class of serial dependence models that permits likelihood-based marginal regression analysis of binary response data. The methods naturally extend the first-order Markov models of Azzalini (1994, Biometrika 81, 767-775) and prove computationally feasible for long series.

摘要

边际广义线性模型现在经常用于纵向数据的分析。Liang和Zeger（1986年，《生物统计学》73卷，第13 - 22页）引入了边际模型的半参数推断方法。本文开发了一类一般的参数化序列依赖模型，该模型允许对二元响应数据进行基于似然的边际回归分析。这些方法自然地扩展了Azzalini（1994年，《生物计量学》81卷，第767 - 775页）的一阶马尔可夫模型，并证明对于长序列在计算上是可行的。