Power and Level Robustness of A Composite Hypothesis Testing under Independent Non-Homogeneous Data

Abstract

Robust tests of general composite hypothesis under non-identically distributed observations is always a challenge. Ghosh and Basu (2018, Statistica Sinica, 28, 1133--1155) have proposed a new class of test statistics for such problems based on the density power divergence, but their robustness with respect to the size and power are not studied in detail. This note fills this gap by providing a rigorous derivation of power and level influence functions of these tests to theoretically justify their robustness. Applications to the fixed-carrier linear regression model are also provided with empirical illustrations.

arXiv commentPre-print, Under review

References in corpus (3)

• Robust Estimation in Generalised Linear Models : The Density Power Divergence Approach
• Robust Inference under the Beta Regression Model with Application to Health Care Studies
• Robust Bounded Influence Tests for Independent Non-Homogeneous Observations