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題名 On simultaneously identifying outliers and heteroscedasticity without specific form
作者 鄭宗記
Cheng,Tsung-Chi
貢獻者 統計系
日期 2012.07
上傳時間 6-十一月-2014 18:22:20 (UTC+8)
摘要 Assuming homogeneous variance in a normal regression model is not always appropriate as invalid standard inference procedures may result from the improper estimation of the standard error when the disturbance process in a regression model presents heteroscedasticity. When both outliers and heteroscedasticity exist, the inflation of the scale’s estimate can deteriorate. Using graphical analysis, this study identifies outliers under heteroscedastic error without specifying a functional form. A jigsaw plot with two kinds of cut-off points differentiates both outlying and heteroscedastic characteristics for each observation in the data. The proposed approach is based on the concept of the weighted least absolute deviation estimator. Furthermore, plugging the resulting residuals into the estimation of the heteroscedasticity-consistent covariance matrix leads to a robust quasi-t test for the estimated coefficients.
關聯 Computational Statistics and Data Analysi56(7),2258-2272
資料類型 article
DOI http://dx.doi.org/http://dx.doi.org/10.1016/j.csda.2012.01.004
dc.contributor 統計系en_US
dc.creator (作者) 鄭宗記zh_TW
dc.creator (作者) Cheng,Tsung-Chien_US
dc.date (日期) 2012.07en_US
dc.date.accessioned 6-十一月-2014 18:22:20 (UTC+8)-
dc.date.available 6-十一月-2014 18:22:20 (UTC+8)-
dc.date.issued (上傳時間) 6-十一月-2014 18:22:20 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/71193-
dc.description.abstract (摘要) Assuming homogeneous variance in a normal regression model is not always appropriate as invalid standard inference procedures may result from the improper estimation of the standard error when the disturbance process in a regression model presents heteroscedasticity. When both outliers and heteroscedasticity exist, the inflation of the scale’s estimate can deteriorate. Using graphical analysis, this study identifies outliers under heteroscedastic error without specifying a functional form. A jigsaw plot with two kinds of cut-off points differentiates both outlying and heteroscedastic characteristics for each observation in the data. The proposed approach is based on the concept of the weighted least absolute deviation estimator. Furthermore, plugging the resulting residuals into the estimation of the heteroscedasticity-consistent covariance matrix leads to a robust quasi-t test for the estimated coefficients.en_US
dc.format.extent 713247 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Computational Statistics and Data Analysi56(7),2258-2272en_US
dc.title (題名) On simultaneously identifying outliers and heteroscedasticity without specific formen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/j.csda.2012.01.004en_US
dc.doi.uri (DOI) http://dx.doi.org/http://dx.doi.org/10.1016/j.csda.2012.01.004en_US