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	Identification of graphical models for nonignorable nonresponse of binary outcomes in longitudinal studies
	Ma, WQ ; Geng, Z ; Hu, YH
	2003
关键词	binary outcome directed acyclic graph identifiability longitudinal data nonignorable nonresponse
英文摘要	In this paper, we use directed acyclic graphs (DAGs) with temporal structure to describe models of nonignorable nonresponse mechanisms for binary outcomes in longitudinal studies, and we discuss identification of these models under an assumption that the sequence of variables has the first-order Markov dependence, that is, the future variables are independent of the past variables conditional on the present variables. We give a stepwise approach for checking identifiability of DAG models. For an unidentifiable model, we propose adding completely observed variables such that this model becomes identifiable. (C) 2003 Elsevier Inc. All rights reserved.; Statistics & Probability; SCI(E); 4; ARTICLE; 1; 24-45; 87
语种	英语
出处	SCI
出版者	多变量分析杂志
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/255667]
专题	数学科学学院
推荐引用方式 GB/T 7714	Ma, WQ,Geng, Z,Hu, YH. Identification of graphical models for nonignorable nonresponse of binary outcomes in longitudinal studies. 2003-01-01.

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