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

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Alonso-Revenga, J. and Martin, N. and Pardo Llorente, Leandro (2017) New improved estimators for overdispersion in models with clustered multinomial data and unequal cluster sizes. Statistics and Computing, 27 (1). pp. 193-217. ISSN 0960-3174

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Official URL: https://link.springer.com/article/10.1007/s11222-015-9616-z


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Abstract

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.


Item Type:Article
Uncontrolled Keywords:Clustered multinomial data; Consistent intracluster correlation estimator; Log-linear model; Overdispersion; Quasi minimum divergence estimator
Subjects:Sciences > Mathematics > Operations research
ID Code:42059
Deposited On:05 Apr 2017 10:14
Last Modified:05 Apr 2017 10:40

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