A Robust Generalization of the Rao Test

TL;DR

This paper introduces robust Rao-type test statistics based on minimum density power divergence estimators, offering improved robustness and theoretical properties for hypothesis testing compared to classical methods.

Contribution

It develops new Rao-type tests using minimum density power divergence estimators, providing robust alternatives with proven asymptotic distributions and robustness properties.

Findings

Proposed tests have robust properties against data contamination.

Asymptotic null distributions are derived for the new tests.

Numerical results demonstrate the effectiveness of the proposed tests.

Abstract

This paper presents new families of Rao-type test statistics based on the minimum density power divergence estimators which provide robust generalizations for testing simple and composite null hypotheses. The asymptotic null distributions of the proposed tests are obtained and their robustness properties are also theoretically studied. Numerical illustrations are provided to substantiate the theory developed. On the whole, the proposed tests are seen to be excellent alternatives to the classical Rao test.