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A Comparison of Five Robust Regression Methods with Ordinary Least Squares: Relative Efficiency, Bias and Test of the Null Hypothesis

Description: A Monte Carlo simulation was used to generate data for a comparison of five robust regression estimation methods with ordinary least squares (OLS) under 36 different outlier data configurations. Two of the robust estimators, Least Absolute Value (LAV) estimation and MM estimation, are commercially available. Three authormodified variations on MM were also included (MM1, MM2, and MM3). Design parameters that were varied include sample size (n=60 and n=180), number of independent predictor variables (2, 3 and 6), outlier density (0%, 5% and 15%) and outlier location (2x,2y s, 8x8y s, 4x,8y s and 8x,4y s). Criteria on which the regression methods were measured are relative efficiency, bias and a test of the null hypothesis. Results indicated that MM2 was the best performing robust estimator on relative efficiency. The best performing estimator on bias was MM1. The best performing regression method on the test of the null hypothesis was MM2. Overall, the MM-type robust regression methods outperformed OLS and LAV on relative efficiency, bias, and the test of the null hypothesis.
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Date: August 2001
Creator: Anderson, Cynthia, 1962-

Taoism and Contemporary Environmental Literature

Description: This thesis encompasses a survey of contemporary environmental literature (1970s to the present) as it relates to the tenets of Taoist literature, specifically the Chuang Tzu and the Tao te Ching. The thesis also presents and evaluates pertinent criticisms concerning the practice of relating modern environmental problems to ancient Chinese philosophy. The thesis contains a preface that describes the historic roots of Taoism as well as an explanation of the Chinese terminology in the paper. The environmental literature is divided into three major groups and discussed in the three chapters of the paper. The three groups include mainstream environmentalists, deep ecology, and ecofeminism.
Date: December 2001
Creator: Kane, Virginia M.