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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/9996

Title: Robust Procedure for the Fit of Oneway Analysis of Variance (ANOVA) models under uncorrelated errors
Authors: Afrifa-Yamoah, Ebenezer
Issue Date: 19-Jan-2017
Abstract: The possible dominance of basic assumption about underlying models on the analysis of data is of much concern to some statisticians (Anscombe (1967);Hogg (1974); B uning (1996)). The advocacy of distribution-free (nonparametric) tests for di erences in location problems between samples has been emphasized over the past seven decades (Hao and Houser, 2011). This study develops a robust tting procedure for one-way ANOVA models. Further investigation on Asymptotic Relative E ciency (ARE) of this procedure and parametric F-test under class of distributions was carried out. In line with these objectives, 10,000 simulations were carried out for a one-way ANOVA model with three levels for size 5, 10, 15, and 20. Intralevel correlation coe cient = 0 was considered in these simulations. The ndings revealed that the parametric F-test for Oneway ANOVA model performed better than the non-parametric Adaptive test proposed for symmetric and moderate tailed distributions and then in symmetric and light tailed distributions with ARE between 2% and 55%. However, the Adaptive test outperformed the F-test in symmetric and asymmetric with varying tail weights distributions with ARE between 5% and 70%. Although, the F-test displayed superiority in e ciency in symmetric medium and light tailed distributions, the Adaptive test was more effi cient in more broader class of continuous distribution.
Description: A thesis submitted to the Department of Mathematics, Kwame Nkrumah University of Science and Technology in partial fulfillment of the requirement for the Degree of Master of Philosophy in Mathematical Statistics, 2016
URI: http://hdl.handle.net/123456789/9996
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