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政大典藏 > College of Commerce > Department of Statistics > Periodical Articles > Item 140.119/76045

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/76045

Title:	Robust diagnostics for the heteroscedastic regression model
Authors:	Cheng, Tsung-Chi 鄭宗記
Contributors:	統計系
Keywords:	Heteroscedasticity;Outlier;Residual maximum likelihood;Robust diagnostics;Search Algorithms;Trimmed likelihood;Estimation;Learning algorithms;Regression analysis;Maximum likelihood estimation
Date:	2011-04
Issue Date:	2015-06-22 16:04:30 (UTC+8)
Abstract:	The assumption of equal variance in the normal regression model is not always appropriate. Cook and Weisberg (1983) provide a score test to detect heteroscedasticity, while Patterson and Thompson (1971) propose the residual maximum likelihood (REML) estimation to estimate variance components in the context of an unbalanced incomplete-block design. REML is often preferred to the maximum likelihood estimation as a method of estimating covariance parameters in a linear model. However, outliers may have some effect on the estimate of the variance function. This paper incorporates the maximum trimming likelihood estimation (Hadi and Luceo, 1997; Vandev and Neykov, 1998) in REML to obtain a robust estimation of modelling variance heterogeneity. Both the forward search algorithm of Atkinson (1994) and the fast algorithm of Neykov et al. (2007) are employed to find the resulting estimator. Simulation and real data examples are used to illustrate the performance of the proposed approach. © 2010 Published by Elsevier B.V.
Relation:	Computational Statistics and Data Analysis, 55(4), 1845-1866
Data Type:	article
DOI link:	http://dx.doi.org/10.1016/j.csda.2010.11.024
DOI:	10.1016/j.csda.2010.11.024
Appears in Collections:	[Department of Statistics] Periodical Articles

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