On divergence tests for composite hypotheses under composite likelihood

Robust Procedures for Estimating and Testing in the Framework of Divergence Measures

The approach for estimating and testing based on divergence measures has become, in the last 30 years, a very popular technique not only in the field of statistics, but also in other areas, such as machine learning, pattern recognition, etc [...].

Model Selection in a Composite Likelihood Framework Based on Density Power Divergence

This paper presents a model selection criterion in a composite likelihood framework based on density power divergence measures and in the composite minimum density power divergence estimators, which depends on an tuning parameter α . After introducing such a criterion, some asymptotic properties are established. We present a simulation study and two numerical examples in order to point out the robustness properties of the introduced model selection criterion.

New Developments in Statistical Information Theory Based on Entropy and Divergence Measures

In the last decades the interest in statistical methods based on information measures and particularly in pseudodistances or divergences has grown substantially [...].