An Exact and Near-Exact Distribution Approach to the Behrens–Fisher Problem

Abstract

The Behrens–Fisher problem occurs when testing the equality of means of two normal distributions without the assumption that the two variances are equal. This paper presents approaches based on the exact and near-exact distributions for the test statistic of the Behrens–Fisher problem, depending on different combinations of even or odd sample sizes. We present the exact distribution when both sample sizes are odd and the near-exact distribution when one or both sample sizes are even. The near-exact distributions are based on a finite mixture of generalized integer gamma (GIG) distributions, used as an approximation to the exact distribution, which consists of an infinite series. The proposed tests, based on the exact and the near-exact distributions, are compared with Welch’s t-test through Monte Carlo simulations, in particular for small and unbalanced sample sizes. The results show that the proposed approaches are competent solutions to the Behrens–Fisher problem, exhibiting precise sizes and better powers than Welch’s approach for those cases. Numerical studies show that the Welch’s t-test tends to be a bit more conservative than the test statistics based on the exact or near-exact distribution, in particular when sample sizes are small and unbalanced, situations in which the proposed exact or near-exact distributions obtain higher powers than Welch’s t test.

A Survey of Deep Learning for Alzheimer’s Disease

“…Liu et al [65] proposed the Multifold Bayesian Kernelization (MBK) algorithm, where a Bayesian framework derives kernel weights and synthesis analysis provides the diagnostic probabilities of each biomarker. Zhang et al [66] proposed the extraction of the eigenbrain using Welch's t-test (WTT) [67] combined with a polynomial kernel SVM [68] and particle swarm optimization (PSO) [69].…”