Maximum Trimmed Likelihood Estimator for Categorical Data Analysis

범주형 자료분석을 위한 최대절사우도추정

  • Choi, Hyun-Jip (Dept. of Applied Information Statistics, Kyonggi Univ.)
  • 최현집 (경기대학교 응용정보통계학과)
  • Published : 2009.03.30


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.


  1. 최현집 (2003). 범주형 자료 분석을 위한 LAD 추정량, <응용통계연구>, 16, 55-69
  2. 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
  3. CIzek, P. (2006). Trimmed likelihood-based estimation in binary regression models, Austrian Journal of Statistics, 2 & 3, 223-232
  4. Grizzle, J. E., Stamer, C. F. and Koch, G. G. (l969). Analysis of categorical data by linear models, Biometrics, 25, 489-504
  5. 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
  6. Mili, L. and Coakley, C. W. (1996). Robust estimation in structured linear regression, The Annals of Statis-tics, 24, 2593-2607
  7. 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
  8. 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
  9. 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
  10. Rousseeuw, P. J. (1984). Least median of squares regression, Journal of the American Statistical Associa-tion, 79, 871-880
  11. Rousseeuw, P. J. and Driessen, K. (2006). Computing LTS regression for large data sets, Data Mining and Knowledge Discovery, 12, 29-45
  12. Shane, K. V. and Simonoff, S. S. (2001). A Robust approach to categorical data analysis, Journal of Computational and Graphical Analysis, 10, 135-157