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

Ayanendranath Basu (), Abhik Ghosh, Nirian Martin and Leandro Pardo
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Ayanendranath Basu: Indian Statistical Institute
Abhik Ghosh: Indian Statistical Institute
Nirian Martin: Complutense University
Leandro Pardo: Complutense University

Metrika: International Journal for Theoretical and Applied Statistics, 2018, vol. 81, issue 5, No 2, 493-522

Abstract: 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.

Keywords: Non-homogeneous data; Robust hypothesis testing; Wald-type test; Minimum density power divergence estimator; Power influence function; Linear regression; Poisson regression (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00184-018-0653-4

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