Preliminary Test Estimators and Phi-divergence Measures in Pooling Binomial Data

Minimum Phi-Divergence Estimators and Phi-Divergence Test Statistics in Contingency Tables with Symmetry Structure: An Overview

In the last years minimum phi-divergence estimators (MφE) and phi-divergence test statistics (φTS) have been introduced as a very good alternative to classical likelihood ratio test and maximum likelihood estimator for different statistical problems. The main purpose of this paper is to present an overview of the main results presented until now in contingency tables with symmetry structure on ...

Wald type and phi-divergence based test-statistics for isotonic binomial proportions

A New Family of BAN Estimators for Polytomous Logistic Regression Models based on phi- Divergence Measures

In this paper we study polytomous logistic regression model and the asymptotic properties of the minimum φ-divergence estimators for this model. A simulation study is conducted to analyze the behavior of these estimators as function of the power-divergence measure φ(λ). AMS 2001 Subject Classification Primary 62H15 · Secondary 62H17

Pooling overdispersed binomial data to estimate event rate

BACKGROUND The beta-binomial model is one of the methods that can be used to validly combine event rates from overdispersed binomial data. Our objective is to provide a full description of this method and to update and broaden its applications in clinical and public health research. METHODS We describe the statistical theories behind the beta-binomial model and the associated estimation metho...

Phi-Divergence Constrained Ambiguous Stochastic Programs for Data-Driven Optimization

This paper investigates the use of φ-divergences in ambiguous (or distributionally robust) two-stage stochastic programs. Classical stochastic programming assumes the distribution of uncertain parameters are known. However, the true distribution is unknown in many applications. Especially in cases where there is little data or not much trust in the data, an ambiguity set of distributions can be...