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

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• 作者：J. M. Alonso-Revenga ; N. Martín ; L. Pardo
• 关键词：Clustered multinomial data ; Consistent intracluster correlation estimator ; Log ; linear model ; Overdispersion ; Quasi minimum divergence estimator
• 刊名：Statistics and Computing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：27
• 期：1
• 页码：193-217
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics and Computing/Statistics Programs; Artificial Intelligence (incl. Robotics); Statistical Theory and Methods; Probability and Statistics in Computer Science;
• 出版者：Springer US
• ISSN：1573-1375
• 卷排序：27

文摘

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.