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Please use this identifier to cite or link to this item: https://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 link:	http://dx.doi.org/10.1016/j.csda.2007.06.026
DOI:	10.1016/j.csda.2007.06.026
Appears in Collections:	[Department of Statistics] Periodical Articles

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