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http://dx.doi.org/10.5351/CKSS.2009.16.2.229

Maximum Trimmed Likelihood Estimator for Categorical Data Analysis  

Choi, Hyun-Jip (Dept. of Applied Information Statistics, Kyonggi Univ.)
Publication Information
Communications for Statistical Applications and Methods / v.16, no.2, 2009 , pp. 229-238 More about this Journal
Abstract
We propose a simple algorithm for obtaining MTL(maximum trimmed likelihood) estimates. The algorithm finds the subset to use to obtain the global maximum in the series of eliminating process which depends on the likelihood of cells in a contingency table. To evaluate the performance of the algorithm for MTL estimators, we conducted simulation studies. The results showed that the algorithm is very competitive in terms of computational burdens required to get the same or the similar results in comparison with the complete enumeration.
Keywords
Contingency table; outlying cell; maximum trimmed likelihood estimator;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Hadi, A. S. and Luceno, A. (1997). Matimum trimmed likelihood estimators: A unified approach, exam-ples, and algorithms, Computational Statistics and Data Analysis, 25, 251-272   DOI   ScienceOn
2 Mili, L. and Coakley, C. W. (1996). Robust estimation in structured linear regression, The Annals of Statis-tics, 24, 2593-2607   DOI   ScienceOn
3 Mosteller, F. and Parunak, A. (1985). Identifying extreme cells in a sizable contingency table: Probabilistic and exploratory approaches, In Exploring Datat Tables, Trends and Shapes, 189-225
4 Neykov, N. and Muller, C. H. (2002). Breakdown point and computation of trimmed likelihood estimators in generalized linear models, In Developments in Robust Statistics, 277-286
5 Neykov, N., Filzmoser, P., Dimova, R. and Neytchev, P. (2007). Robust fitting of mixtures using the trimmed likelihood estimator, Computational Statistics and Data Analysis, 52, 299-308   DOI   ScienceOn
6 Rousseeuw, P. J. (1984). Least median of squares regression, Journal of the American Statistical Associa-tion, 79, 871-880   DOI   ScienceOn
7 Rousseeuw, P. J. and Driessen, K. (2006). Computing LTS regression for large data sets, Data Mining and Knowledge Discovery, 12, 29-45   DOI   ScienceOn
8 Shane, K. V. and Simonoff, S. S. (2001). A Robust approach to categorical data analysis, Journal of Computational and Graphical Analysis, 10, 135-157   DOI   ScienceOn
9 Cheng, T. and Biswas, A. (2008). Maximum trimmed likelihood estimator for multivariate mixed contin-uous and categorical data, Computational Statistics and Data Analysis, 52, 2042-2065   DOI   ScienceOn
10 최현집 (2003). 범주형 자료 분석을 위한 LAD 추정량, <응용통계연구>, 16, 55-69   과학기술학회마을   DOI
11 CIzek, P. (2006). Trimmed likelihood-based estimation in binary regression models, Austrian Journal of Statistics, 2 & 3, 223-232
12 Grizzle, J. E., Stamer, C. F. and Koch, G. G. (l969). Analysis of categorical data by linear models, Biometrics, 25, 489-504   DOI   ScienceOn