FITTING PARAMETRIC COUNTING-PROCESSES BY USING LOG-LINEAR MODELS

被引:22
作者
LINDSEY, JK [1 ]
机构
[1] LIMBURGS UNIV,DIEPENBEEK,BELGIUM
来源
APPLIED STATISTICS-JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C | 1995年 / 44卷 / 02期
关键词
ADDITIVE INTENSITIES; COUNTING PROCESS; EXPONENTIAL DISTRIBUTION; EXTREME VALUE DISTRIBUTION; INTENSITY FUNCTION; LOG-LINEAR MODEL; MULTIPLICATIVE INTENSITIES; NONHOMOGENEOUS STOCHASTIC PROCESS; POISSON REGRESSION; PROPORTIONAL HAZARDS MODEL; SURVIVAL DATA; TIME VARYING COVARIATES; WEIBULL DISTRIBUTION;
D O I
10.2307/2986345
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
Counting processes constitute a means of describing how and when a series of events occurs to individuals. The risk or intensity of events, which may vary over time, can depend on any aspects of the previous history of the individual. Standard log-linear regression modelling techniques are used to choose from the explanatory variables those which are appropriate to describe this dependence on the past. Details are given on how to set up such repeated measurements of duration among events as log-linear models. Two examples show how the technique can be used, even for simple survival data, to choose between models of different complexity and highlight the importance of dependence on the past for repeated events such as infection due to chronic granulotomous disease in the study of the effect of gamma interferon treatment.
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页码:201 / 212
页数:12
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