Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/33296
Title: Some new methods for comparing several sets of regression coefficients under heteroscedasticity
Authors: YONG YEE MAY
Keywords: heteroscedasticity, parametric bootstrap, approximate degrees of freedom test, Wald statistic, Chow’s test, linear models
Issue Date: 7-Mar-2012
Source: YONG YEE MAY (2012-03-07). Some new methods for comparing several sets of regression coefficients under heteroscedasticity. ScholarBank@NUS Repository.
Abstract: The Chow?s test was proposed to test the equivalence of coefficients of two linear regression models under the assumption of equal variances. However, studies have shown that his test may produce inaccurate results in the presence of heteroscedasticity. Subsequently, Conerly and Manfield modified his test to cater for unequal variances of two linear regression models. We generalize this modified Chow?s test to k-sample case. Zhang has also proposed a wald-type statistics, namely the approximate degrees of freedom test, to test the equality of the coefficients of k linear regression models with unequal variances. A parametric bootstrap (PB) approach will be proposed to test the equivalence of coefficients of k linear models for heteroscedastic case. Simulation studies and real data application are presented to compare and examine the performances of these test statistics.
URI: http://scholarbank.nus.edu.sg/handle/10635/33296
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