Semiparametric analysis of two-level bivariate binary data

Biometrics

Volumn 62, Issue 4, 2006, Pages 1004-1013

  Naskar, Malay ^a   Das, Kalyan ^a  

^a UNIVERSITY OF CALCUTTA   (India)

Author keywords

Bivariate binary; Dirichlet process; MCEM; Semiparametric model

Indexed keywords

IMAGE SEGMENTATION; MAXIMUM PRINCIPLE;

BINARY DATA; BIVARIATE; BIVARIATE BINARY; DIRICHLET PROCESS; HYBRID METHOD; MCEM; MEDICAL STUDIES; SEMI-PARAMETRIC MODELING; SEMIPARAMETRIC; UNIVARIATE;

MONTE CARLO METHODS;

TIBOLONE;

ALGORITHM; METHODOLOGY; MONTE CARLO ANALYSIS; NUMERICAL MODEL;

ALGORITHM; ARTICLE; DRUG EFFICACY; HUMAN; INDIA; MENOPAUSAL SYNDROME; MULTIVARIATE ANALYSIS; STATISTICAL MODEL; SYMPTOMATOLOGY;

EID: 33845502070     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2006.00618.x     Document Type: Article

References (19)

1. Antoniak, C. (1974). Mixture of Dirichlet process with application to nonparametric problems. Annals of Statistics 2, 1152-1174.
2. Blackwell, D. and MacQueen, J. (1973). Ferguson distributions via Polya urn schemes. Annals of Statistics 1, 353-355.
3. Dale, J. (1986). Global cross-ratio models for bivariate discrete ordered responses. Biometrics 41, 909-917.
4. Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. Annals of Statistics 1, 209-230.
5. Fitzmaurice, G. M., Molenberghs, G., and Lipsitz, S. R. (1995). Regression models for binary responses with information drop-outs. Journal of the Royal Statistical Society, Series B 57, 691-704.
6. Gelfand, A. and Kottas, A. (2002). A computational approach for full nonparametric Bayesian inference under Dirichlet process mixture models. Journal of Computational and Graphical Statistics 11, 289-305.
7. Ishwaran, H. and James, L. F. (2001). Gibbs sampling methods for stick breaking priors. Journal of the American Statistical Association 96, 161-173.
8. Ishwaran, H. and Zarepour, M. (2000). Markov chain Monte Carlo in approximate Dirichlet and beta two-parameter process hierarchical models. Biometrika 87, 371-390.
9. Lang, J. B. and Agresti, A. (1994). Simultaneously modeling joint and marginal distributions of multivariate categorical responses. Journal of the American Statistical Association 89, 625-632.
10. Li, C., Wilawan, K., Sansiuie, G., Lidfeldt, J., Agardh, C., and Nerbrand, C. (2002). Health profile of middle-aged women: The Women's Health in the Lund Area (WHILA) study. Human Reproduction 17, 1379-1385.
11. Louis, T. (1982). Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society, Series B 44, 226-233.
12. McCullagh, P. and Nelder, J. (1989). Generalized Linear Models, 2nd edition. London: Chapman and Hall.
13. Molenberghs, G. and Lesaffre, E. (1994). Marginal modeling of correlated ordinal data using Plackett distribution. Journal of the American Statistical Association 89, 633-644.
14. Naskar, M. and Das, K. (2005). Semiparametric analysis of two level bivariate binary data. Technical Report, Department of Statistics, University of Calcutta, Kolkata, India.
15. Neal, R. M. (2000). Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics 9, 249-265.
16. Neuhaus, J. M., Kalbfleisch, J. D., and Hauck, W. W. (1991). A comparison of cluster-specific and population-averaged approaches for analysing correlated binary data. International Statistical Review 59, 25-35.
17. TenHave, T. R. and Morabia, A. (1999). Mixed effect models with bivariate and univariate association parameters for longitudinal bivariate binary response data. Biometrics 55, 85-93.
18. West, M., Muller, P., and Escober, M. D. (1994). Hierarchical priors and mixture models, with application in regression and density estimation. In Aspect of Uncertainty, Volume 3, P. R. Freeman and A. F. M. Smith (eds), 363-386. New York: Wiley.
19. Zeger, S. L., Liang, K. Y., and Albert, P. A. (1988). Models for longitudinal data: A generalized estimating equation approach. Biometrics 44, 1049-1060.