Closure of the class of binary generalized linear models in some non-standard settings
成果类型：
Article
署名作者：
Neuhaus, JM
署名单位：
University of California System; University of California San Francisco
刊物名称：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
ISSN/ISSBN：
1369-7412
DOI：
10.1111/1467-9868.00268
发表日期：
2000
页码：
839-846
关键词：
摘要：
This paper considers fitting generalized linear models to binary data in non-standard settings such as case-control samples, studies with misclassified responses and misspecified models. We develop simple methods for fitting models to case-control data and show that a closure property holds for generalized linear models in the non-standard settings, i.e., if the responses follow a generalized linear model in the population of interest, then so will the observed response in the non-standard setting. but with a modified link function. These results imply that we can analyse data and study problems in the non-standard settings by using classical generalized linear model methods such as the iteratively reweighted least squares algorithm. Example data illustrate the results.
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