Robust Wald-type tests for non-homogeneous observations based on the minimum density power divergence estimator

Authors

Ayanendranath Basu, Indian Statistical Institute, Kolkata
Abhik Ghosh, Indian Statistical Institute, Kolkata
Nirian Martin, Universidad Complutense de Madrid
Leandro Pardo, Universidad Complutense de Madrid

Article Type

Research Article

Publication Title

Metrika

Abstract

This paper considers the problem of robust hypothesis testing under non-identically distributed data. We propose Wald-type tests for both simple and composite hypothesis for independent but non-homogeneous observations based on the robust minimum density power divergence estimator of the common underlying parameter. Asymptotic and theoretical robustness properties of the proposed tests are discussed. Application to the problem of testing for the general linear hypothesis in a generalized linear model with a fixed-design has been considered in detail with specific illustrations for its special cases under the normal and Poisson distributions.

First Page

493

Last Page

522

DOI

10.1007/s00184-018-0653-4

Publication Date

7-1-2018

Comments

All Open Access, Green

Recommended Citation

Basu, Ayanendranath; Ghosh, Abhik; Martin, Nirian; and Pardo, Leandro, "Robust Wald-type tests for non-homogeneous observations based on the minimum density power divergence estimator" (2018). Journal Articles. 1327.
https://digitalcommons.isical.ac.in/journal-articles/1327