EM estimation for finite mixture models with known mixture component size

Communications in Statistics: Simulation and Computation

Volumn 44, Issue 6, 2015, Pages 1545-1556

  Teel, Chen ^a   Park, Taeyoung ^b   Sampson, Allan R ^c  

^a DUPONT COMPANY   (United States)

^b Yonsei University   (South Korea)

^c UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

Aggregate data; Conditional Bernoulli distribution; EM algorithm; Finite mixture models

Indexed keywords

AGGREGATES;

AGGREGATE DATUM; BERNOULLI DISTRIBUTIONS; EM ALGORITHMS; ESTIMATES OF PARAMETERS; FINITE MIXTURE MODELS; MIXTURE COMPONENTS; STABLE CONVERGENCE;

MIXTURES;

EID: 84918561994     PISSN: 03610918     EISSN: 15324141     Source Type: Journal    
DOI: 10.1080/03610918.2013.824091     Document Type: Article

References (20)

1. Basford, K. E., McLachlan, G. J. (1985). Likelihood estimation with normal mixture models. Journal of Applied Statistics 34: 282-289
2. Chen, X., Dempster, A. P., Liu, J. (1994). Weighted finite population sampling to maximize entropy. Biometrika 81: 457-469
3. Chen, X., Liu, J. (1997). Statistical application of the poisson-binomial and conditional Bernoulli distributions. Statistica Sinica 7: 875-892
4. Chen, Y., Yang, S. (2007). Estimating disaggregate models using aggregate data through augmentation of individual choice. Journal of Marketing Research 44: 613-621
5. Choi, T., Schervish, M. J., Schmitt, K. A., Small, M. J. (2008). A Bayesian approach to logistic regression model with incomplete information. Biometrics 64: 424-430
6. Dempster, A. P., Laird, N. M., Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society Series B 39: 1-38
7. Friede, T., Kieser, M. (2001).Acomparison ofmethods for adaptive sample size adjustment. Statistics in Medicine 20: 3861-3873
8. Friede, T., Kieser, M. (2002). On the inappropriateness of an EM algorithm based procedure for blinded sample size re-estimation. Statistics in Medicine 21: 165-176
9. Gail, M. H., Lubin, J. H., Rubinstein, L. V. (1981). Likelihood calculation for matched case-control studies and survival studies with tied death times. Biometrika 68: 703-707
10. Gould, A. L., Shih, W. J. (1992). Sample size re-estimationwithout unblinding for normally distributed outcomes with unknown variance. Communications in Statistics 21: 2833-2853
11. McLachlan, G. J., Peel, D. (2000). Finite Mixture Models. New York: Wiley
12. Musalem, A., Bradlow, E. T., Raju, J. S. (2009). Bayesian estimation of random-coefficients choice models using aggregate data. Journal of Applied Econometrics 24: 490-516
13. Nettleton, D. (1999). Convergence properties of the EM algorithm in constrained parameter spaces. The Canadian Journal of Statistics 27: 639-648
14. Park, T. (2011). Bayesian analysis of individual choice behavior with aggregate data. The Journal of Computational and Graphical Statistics 20: 158-173
15. Rosenberger, W. F., Lachin, J. M. (2002). Randomization in Clinical Trials: Theory and Practice. New York: Wiley, pp 81-83
16. Stephens, M. (2000). Dealing with label switching in mixture models. Journal of Royal Statistical Society 62: 795-809
17. Teel, C. (2011). Improved sample size reestimation in adaptive clinical trials without unblinding. Ph.D. thesis, Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania
18. Verhelst, N.D. (2008).An efficientMCMCalgorithm to sampler binary matrices with fixed marginals. Psychometrika 73: 705-728
19. Xing, B., Ganju, J. (2005). A method to estimate the variance of an endpoint from an on-going blinded trial. Statistics in Medicine 24: 1807-1814
20. Zucker, D. M., Wittes, J. T., Schabenberger, O., Brittain, E. (1999). Internal pilot studies II : comparison of various procedure. Statistics in Medicine 18: 3493-3509