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Maximum likelihood estimation for a mixture distribution

이항-퇴화 혼합분포의 최우추정법

  • Received : 2015.01.17
  • Accepted : 2015.03.18
  • Published : 2015.03.31

Abstract

A mixture distribution of a discrete uniform or degenerated distribution and two binomial distribution is proposed and a method of obtaining the maximum likelihood estimates of the parameters is provided. For the proposed model simulation studies were conducted to see performance of the maximum likelihood estimates and a mixture of a degenerated distribution and two binomial distributions was applied to fit a lecture evaluation data to show a good result.

본 연구에서는 하나의 균일분포 또는 퇴화분포와 두 개의 이항분포의 혼합분포 모형에 대하여 최우추정법을 소개하며, 제시된 모형에 대하여 시뮬레이션을 통해 최우추정량의 성질을 밝히며, 실험을 통해 얻은 강의 평가 자료에 대하여 퇴화분포를 가지는 혼합분포에 대하여 적용하여 보았다. 특히 퇴화분포는 한국의 문화 특성상 가운데 값을 선호하는 현상을 모형화하는데 유용하게 사용될 수 있음을 보였다.

Keywords

References

  1. Beran, T. and Violato, C. (2005). Ratings of university teacher instruction: how much do student and course characteristics really matter? Assessment & Evaluation in Higher Education, 30, 593-601. https://doi.org/10.1080/02602930500260688
  2. Blischke, W. (1964). Estimating the parameters of mixture of binomial distributions. Journal of the American Statistical Association, 59, 510-528. https://doi.org/10.1080/01621459.1964.10482176
  3. Bonnini, S., Piccolo, D., Salmaso, L. and Solmi, F. (2012). Permutation inference for a class of mixture models. Communications in Statistics-Theory and Methods, 41, 2879-2895. https://doi.org/10.1080/03610926.2011.590915
  4. Cicia, G., Corduas, M., Giudice, T. D. and Piccolo, D. (2010). Valuing consumer preferences with the CUB model: A case study of fair trade coffee. International Journal on Food System Dynamics, 1, 82-93.
  5. Corduas, M. (2011). A study on university students' opinions about teaching quality: a model based approach for clustering ordinal data. In M. Attanasio & V. Capursi Jackson (Eds.), Statistical Methods for the Evaluation of University Systems. Heidelberg: Springer.
  6. Cekanavicius, V., Pekoz, E. A., Rollin, A. and Shwartz, M. (2009). A three-parameter binomial approximation. Available from http://arxiv.org/abs/0906.2855. https://doi.org/10.1239/jap/1261670689
  7. Copsey, K. and Webb, A. (2003). Bayesian gamma mixture model approach to radar target recognition. IEEE Transactions on Aerospace and Electronic Systems, 39, 1201-1217. https://doi.org/10.1109/TAES.2003.1261122
  8. D'Elia, A. and Piccolo, D. (2005). A mixture model for preference data analysis. Computational Statistics and Data Analysis, 49, 917-934. https://doi.org/10.1016/j.csda.2004.06.012
  9. Dempster, A. P., Laird, N. M. and Rubin, D. R. (1977). Maximum likelihood from incomplete data. Journal of the Royal Statistical Society B, 39, 1-38.
  10. Domenico, P. (2003). On the moments of a mixture of uniform and shifted binomial random variables. Quaderni di Statistica, 5, 1-20.
  11. Greenwald, A. G. (2002). Constructs in student ratings of instructors. In H. I. Braun, D. N. Jackson, & D. E. Wiley (Eds.), The role of constructs in psychological and educational measurement. New York:Erlbaum.
  12. Iannario M. (2010). On the identifiability of a mixture model for ordinal data. METRON, LXVIII, 87-94.
  13. Iannario M. (2012a). Preliminary estimators for a mixture model of ordinal data. Adv Data Anal Classif , 5, 163-184.
  14. Iannario M. (2012b). Modelling shelter choices in a class of mixture models for ordinal responses. Stat Methods Appl, 21, 1-22. https://doi.org/10.1007/s10260-011-0176-x
  15. Iannario, M., Manisera, M., Piccolo, D. and Zuccolotto, P. (2012). Sensory analysis in the food industry as a tool for marketing decisions. Adv Data Anal Classif , 6, 303-321. https://doi.org/10.1007/s11634-012-0120-4
  16. Iannario M. and Piccolo D. (2011). CUB Models: Statistical Methods and Empirical Evidence. Modern Analysis of Customer Satisfaction Surveys, Kenett R. S. and Salini S. (Eds). John Wiley and Sons: Chichester: UK.
  17. Johnson, N. L., Kemp, A. W. and Kotz, S. (2005). Univariate discrete distributions, 3rd ed., Wiley-Interscience, New York.
  18. Kenett, R. S. and Salini, S. (2011). Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis. Applied Stochastic Models in Business and Industry, 27, 465-475. https://doi.org/10.1002/asmb.927
  19. Lee, H. J. and Oh, C. (2006). Estimation in mixture of shifted Poisson distributions with known shift parameters. Journal of the Korean Data & Information Science Society, 17, 785-794.
  20. Liu, Z., Almhana, J., Choulakian, V. and McGorman, R. (2006). Online EM algorithm for mixture with application to internet traffic modeling. Computational Statistics & Data Analysis, 50, 1052-1071. https://doi.org/10.1016/j.csda.2004.11.002
  21. McLachlan, G. J. and Krishnan, T. (2008). The EM algorithm and extensions, 2nd ed., Wiley, Hoboken, NJ.
  22. McLachlan, G. J. and Peel, D. (2001). Finite mixture models, John Wiley & Sons, Inc., New York.
  23. Oh, C. (2006). Estimation in mixture of shifted Poisson distributions. Journal of the Korean Data & Information Science Society, 17, 1209-1217.
  24. Oh, C. (2014). A maximum likelihood estimation method for a mixture of shifted binomial distributions. Journal of the Korean Data & Information Science Society, 25, 255-261. https://doi.org/10.7465/jkdi.2014.25.1.255
  25. Piccolo D. (2003). On the moments of a mixture of uniform and shifted binomial random variables. Quaderni di Statistica, 5, 85-104.
  26. Piccolo D. and D'Elia A. (2008). A new approach for modeling consumers' preferences. Food Quality Preference, 19, 247-259. https://doi.org/10.1016/j.foodqual.2007.07.002
  27. Skipper, M. (2012). A Polya approximation to the Poisson-binomial law. Journal of Apply Probability, 49, 745-757. https://doi.org/10.1239/jap/1346955331

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