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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/77960
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/77960


    Title: Maximum trimmed likelihood estimator for multivariate mixed continuous and categorical data
    Authors: Cheng, Tsung-Chi;Biswas, Atanu
    鄭宗記
    Contributors: 統計系
    Keywords: Forward search algorithm;Mahalanobis distance;Maximum trimmed likelihood estimator;Minimum covariance determinant estimator;Mixed data;Multiple outliers;Robust diagnostics
    Date: 2008-01
    Issue Date: 2015-08-24 15:00:37 (UTC+8)
    Abstract: In this article, we apply the maximum trimmed likelihood (MTL) approach [Hadi, A.S., Luceño, A., 1997. Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms. Comput. Statist. Data Anal. 25, 251–272] to obtain the robust estimators of multivariate location and shape, especially for data mixed with continuous and categorical variables. The forward search algorithm [Atkinson, A.C., 1994. Fast very robust methods for the detection of multiple outliers. J. Amer. Statist. Assoc. 89, 1329–1339] is adapted to compute the proposed MTL estimates. A simulation study shows that the proposed estimator outperforms the classical maximum likelihood estimator when outliers exist in data. Real data sets are also used to illustrate the method and results of the detection of the outliers.
    Relation: Computational Statistics & Data Analysis, 52(4), 2042-2065
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/j.csda.2007.06.026
    DOI: 10.1016/j.csda.2007.06.026
    Appears in Collections:[統計學系] 期刊論文

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