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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/46105


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    題名: Robust Diagnostics for Heteroscedastic Regression Model
    作者: 鄭宗記
    Cheng, Tsung-Chi
    貢獻者: 統計系
    關鍵詞: Forward search algorithm;Heteroscedasticity;Maximum trimmed likelihood estimator;Residual maximum likelihood estimator;Outlier;Robust diagnostics
    日期: 2011-04
    上傳時間: 2010-10-06 11:21:44 (UTC+8)
    摘要: 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 Luceño, 1997 and 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.
    關聯: Computational Statistics & Data Analysis, 55(4), 1845-1866
    資料來源: http://dx.doi.org/10.1016/j.csda.2010.11.024
    資料類型: article
    顯示於類別:[統計學系] 期刊論文

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