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    题名: Maximum trimmed likelihood estimator for multivariate mixed continuous and categorical data
    作者: Cheng, Tsung-Chi;Biswas, Atanu
    鄭宗記
    贡献者: 統計系
    关键词: Forward search algorithm;Mahalanobis distance;Maximum trimmed likelihood estimator;Minimum covariance determinant estimator;Mixed data;Multiple outliers;Robust diagnostics
    日期: 2008-01
    上传时间: 2015-08-24 15:00:37 (UTC+8)
    摘要: 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.
    關聯: Computational Statistics & Data Analysis, 52(4), 2042-2065
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1016/j.csda.2007.06.026
    DOI: 10.1016/j.csda.2007.06.026
    显示于类别:[統計學系] 期刊論文

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