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

A. Basu, A. Mandal, N. Martin, L. Pardo

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1-26
Number of pages26
JournalStatistics
Volume50
Issue number1
DOIs
StatePublished - Jan 2 2016

Keywords

  • density power divergence
  • robustness
  • tests of hypotheses

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