New improved estimators for overdispersion in models with clustered multinomial data and unequal cluster sizes

被引：0
|
作者：
J. M. Alonso-Revenga
N. Martín
L. Pardo
机构：
[1] Complutense University of Madrid,Department of Statistics and O.R. III
[2] Complutense University of Madrid,Dapartment of Statistics and Operation Research (Decision Methods)
[3] Complutense University of Madrid,Department of Statistics and O.R. I
来源：
Statistics and Computing | 2017年 / 27卷
关键词：
Clustered multinomial data; Consistent intracluster correlation estimator; Log-linear model; Overdispersion; Quasi minimum divergence estimator;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
It is usual to rely on the quasi-likelihood methods for deriving statistical methods applied to clustered multinomial data with no underlying distribution. Even though extensive literature can be encountered for these kind of data sets, there are few investigations to deal with unequal cluster sizes. This paper aims to contribute to fill this gap by proposing new estimators for the intracluster correlation coefficient.
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页码：193 / 217
页数：24
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