Robust bounded influence tests for independent non-homogeneous observations

Article Type

Research Article

Publication Title

Statistica Sinica

Abstract

Experiments often yield non-identically distributed data for statistical analysis. Tests of hypothesis under such set-ups are generally performed using the likelihood ratio test, which is non-robust with respect to outliers and model misspecification. In this paper, we consider the set-up of non-identically but independently distributed observations and develop a general class of test statistics for testing parametric hypothesis based on the density power divergence. The proposed tests have bounded influence functions, are highly robust with respect to data contamination, have high power against contiguous alternatives, and are consistent at any fixed alternative. The methodology is illustrated by the simple and generalized linear regression models with fixed covariates.

First Page

1133

Last Page

1155

DOI

10.5705/ss.202015.0320

Publication Date

7-1-2018

Comments

All Open Access, Green

This document is currently not available here.

Share

COinS