Robust Tests for the Equality of Two Normal Means based on the Density Power Divergence

Abstract

Statistical techniques are used in all branches of science to determine the feasibility of quantitative hypotheses. One of the most basic applications of statistical techniques in comparative analysis is the test of equality of two population means, generally performed under the assumption of normality. In medical studies, for example, we often need to compare the effects of two different drugs, treatments or preconditions on the resulting outcome. The most commonly used test in this connection is the two sample $t$-test for the equality of means, performed under the assumption of equality of variances. It is a very useful tool, which is widely used by practitioners of all disciplines and has many optimality properties under the model. However, the test has one major drawback; it is highly sensitive to deviations from the ideal conditions, and may perform miserably under model misspecification and the presence of outliers. In this paper we present a robust test for the two sample hypothesis based on the density power divergence measure (Basu et al., 1998), and show that it can be a great alternative to the ordinary two sample $t$-test. The asymptotic properties of the proposed tests are rigorously established in the paper, and their performances are explored through simulations and real data analysis.

20 pages, 9 figures

Cited by in corpus (4)

• A Wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator
• Testing Composite Hypothesis based on the Density Power Divergence
• A new class of robust two-sample Wald-type tests
• Empirical phi-divergence test-statistics for the equalityof means of two populations