Pseudo minimum phi-divergence estimator for multinomial logistic regression with complex sample design

2016/06/03 by Elena Castilla, Castilla, Elena, Nirian Martín +4
Decision Sciences · Mathematics · #Advanced Statistical Methods and Models #Advanced Statistical Process Monitoring #FOS: Computer and information sciences #Methodology (stat.ME) #Survey Sampling and Estimation Techniques #stat.ME

paper · pdf · doi:10.48550/arxiv.1606.01009

arxiv created 2016/06/03 · openalex publication_date 2016/06/03 · arxiv updated 2016/06/06 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

This article develops the theoretical framework needed to study the multinomial logistic regression model for complex sample design with pseudo minimum phi-divergence estimators. Through a numerical example and simulation study new estimators are proposed for the parameter of the logistic regression model with overdispersed multinomial distributions for the response variables, the pseudo minimum Cressie-Read divergence estimators, as well as new estimators for the intra-cluster correlation coefficient. The results show that the Binder's method for the intra-cluster correlation coefficient exhibits an excellent performance when the pseudo minimum Cressie-Read divergence estimator, with lambda = 2/3 , is plugged.

Citations

• Generalized Linear Models

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