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題名 High Breakdown Estimation of Multivariate Mean and covariance With Missing Observations
作者 鄭宗記;Victoria-Feser M.-P.
Cheng, Tsung-Chi;Maria-Pia Victoria-Feser
日期 2002-11
上傳時間 19-Dec-2008 14:51:34 (UTC+8)
摘要 We consider the problem of outliers in incomplete multivariate data when the aim is to estimate a measure of mean and covariance, as is the case, for example, in factor analysis. The ER algorithm of Little and Smith which combines the EM algorithm for missing data and a robust estimation step based on an M-estimator could be used in such a situation. However, the ER algorithm as originally proposed can fail to be robust in some cases, especially in high dimensions. We propose here two alternatives to avoid the problem. One is to combine a small modification of the ER algorithm with a so-called high-breakdown estimator as the starting point for the iterative procedure, and the other is to base the estimation step of the ER algorithm on a high-breakdown estimator. Among the high-breakdown estimators which are actually built to keep their robustness properties even if the number of variables is relatively large, we consider here the minimum covariance determinant estimator and the t-biweight S-estimator. Simulated and real data are used to compare and illustrate the different procedures.
關聯 British Journal of Mathematical and Statistical Psychology, 55,317-335
資料類型 article
DOI http://dx.doi.org/10.1348/000711002760554615
dc.creator (作者) 鄭宗記;Victoria-Feser M.-P.zh_TW
dc.creator (作者) Cheng, Tsung-Chi;Maria-Pia Victoria-Feser-
dc.date (日期) 2002-11en_US
dc.date.accessioned 19-Dec-2008 14:51:34 (UTC+8)-
dc.date.available 19-Dec-2008 14:51:34 (UTC+8)-
dc.date.issued (上傳時間) 19-Dec-2008 14:51:34 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/18156-
dc.description.abstract (摘要) We consider the problem of outliers in incomplete multivariate data when the aim is to estimate a measure of mean and covariance, as is the case, for example, in factor analysis. The ER algorithm of Little and Smith which combines the EM algorithm for missing data and a robust estimation step based on an M-estimator could be used in such a situation. However, the ER algorithm as originally proposed can fail to be robust in some cases, especially in high dimensions. We propose here two alternatives to avoid the problem. One is to combine a small modification of the ER algorithm with a so-called high-breakdown estimator as the starting point for the iterative procedure, and the other is to base the estimation step of the ER algorithm on a high-breakdown estimator. Among the high-breakdown estimators which are actually built to keep their robustness properties even if the number of variables is relatively large, we consider here the minimum covariance determinant estimator and the t-biweight S-estimator. Simulated and real data are used to compare and illustrate the different procedures.-
dc.format application/en_US
dc.language enen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) British Journal of Mathematical and Statistical Psychology, 55,317-335en_US
dc.title (題名) High Breakdown Estimation of Multivariate Mean and covariance With Missing Observationsen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1348/000711002760554615-
dc.doi.uri (DOI) http://dx.doi.org/10.1348/000711002760554615-