Nonparametric Bootstrap Method for Location Testing between Two Populations under Combined Assumption Violations
Abstract
This research aims to compare the efficiency of four nonparametric bootstrap methods for location testing between two populations when the preliminary assumptions are violated. The four methods include Nonparametric Bootstrap t test (NBTT), Nonparametric Bootstrap Welch t test (NBWT), Nonparametric Bootstrap Welch test based on Rank (NBWR), and Nonparametric Bootstrap Yuen Test (NBYT). The data simulation designed to have log-normal, exponential, and gamma distribution. The test includes both equal and unequal of variances and sample size. The results show that when population has log-normal, exponential, and gamma distribution with equal variance, unequal variance and sample size , the NBWR method has the highest efficiency. When and unequal variance ratio of 1:4 and 1:9, the NBYT method has the highest efficiency. In case that the sample size and unequal variance, the NBYT method has the highest efficiency.
Keywords : bootstrap method ; nonparametric test ; location testing
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