Title: 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
Publication Year: 2020
Publication Date: 2020-09-01
Language: en
Type: article
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Cited By Count: 1
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