Generalized Wald-type tests based on minimum density power divergence estimators

Authors

A. Basu, Indian Statistical Institute, Kolkata
A. Mandal, Indian Statistical Institute, Kolkata
N. Martin, Universidad Carlos III de Madrid
L. Pardo, Universidad Complutense de Madrid

Article Type

Research Article

Publication Title

Statistics

Abstract

In testing of hypothesis, the robustness of the tests is an important concern. Generally, the maximum likelihood-based tests are most efficient under standard regularity conditions, but they are highly non-robust even under small deviations from the assumed conditions. In this paper, we have proposed generalized Wald-type tests based on minimum density power divergence estimators for parametric hypotheses. This method avoids the use of nonparametric density estimation and the bandwidth selection. The trade-off between efficiency and robustness is controlled by a tuning parameter β. The asymptotic distributions of the test statistics are chi-square with appropriate degrees of freedom. The performance of the proposed tests is explored through simulations and real data analysis.

First Page

1

Last Page

26

DOI

10.1080/02331888.2015.1016435

Publication Date

1-2-2016

Comments

Open Access; Green Open Access

Recommended Citation

Basu, A.; Mandal, A.; Martin, N.; and Pardo, L., "Generalized Wald-type tests based on minimum density power divergence estimators" (2016). Journal Articles. 4225.
https://digitalcommons.isical.ac.in/journal-articles/4225