Nonparametric inference based on panel count data

被引:9
|
作者
Zhao, Xingqiu [3 ]
Balakrishnan, N. [2 ]
Sun, Jianguo [1 ]
机构
[1] Univ Missouri, Dept Stat, Columbia, MO 65211 USA
[2] McMaster Univ, Dept Math & Stat, Hamilton, ON L8S 4K1, Canada
[3] Hong Kong Polytech Univ, Dept Appl Math, Hong Kong, Hong Kong, Peoples R China
基金
加拿大自然科学与工程研究理事会;
关键词
Bayesian estimation; Generalized least-squares; Mean function; Markov model; Nonparametric comparison; Nonparametric maximum likelihood; Nonparametric maximum pseudo-likelihood; Panel count data; Rate function; RECURRENT EVENT RESPONSES; INTERVAL-CENSORED DATA; REGRESSION-ANALYSIS; POINT-PROCESSES; MEAN FUNCTION; SEMIPARAMETRIC REGRESSION; DEPENDENT OBSERVATION; MULTISTATE MODELS; OBSERVATION TIMES; MARKOV-MODELS;
D O I
10.1007/s11749-010-0223-1
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Panel count data usually refer to data arising from studies on recurrent events in which the subjects under study are followed or observed only periodically rather than continuously. In such situations, an objective of interest is about the occurrence of some events that can occur multiple times or repeatedly and the studies resulting in this type of information are often referred to as event history studies. There are many fields such as medical studies, reliability experiments and social sciences wherein panel count data are encountered commonly. This article reviews basic concepts about panel count data, some common issues and questions of interest regarding them as well as the corresponding statistical procedures that are suitable for their analysis. In particular, we will discuss an estimation of the mean function of the underlying counting process characterizing the occurrence of the events, comparison of several processes and analysis of multiple state panel count data. Some discussion is also presented of situations involving dependent or informative observation processes.
引用
收藏
页码:1 / 42
页数:42
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