Application of H-likelihood to factor analysis models with binary response data

Journal of Multivariate Analysis

Volumn 106, Issue , 2012, Pages 72-79

  Wu, Jianmin ^a,b   Bentler, Peter M ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b BEIJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Binary response; Factor analysis; H likelihood; Marginal likelihood

Indexed keywords

EID: 84862790086     PISSN: 0047259X     EISSN: 10957243     Source Type: Journal    
DOI: 10.1016/j.jmva.2011.09.007     Document Type: Article

References (20)

1. Bartholomew D.J. Factor analysis for categorical data. J. R. Stat. Soc. Ser. B 1980, 42:293-321.
2. Booth J.G., Hobert J.P. Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. J. R. Stat. Soc. Ser. B 1999, 61:265-285.
3. Ha I.D., Lee Y., Song J.K. Hierarchical likelihood approach for frailty models. Biometrika 2001, 88:233-243.
4. Ha I.D., Lee Y., Song J.K. Hierarchical-likelihood approach for mixed linear models with censored data. Lifetime Data Anal. 2002, 8:163-176.
5. Hedeker D., Gibbons R.D. A random-effects ordinal regression model for multilevel analysis. Biometrics 1994, 50:933-944.
6. Hedeker D., Gibbons R.D. MIXOR: a computer program for mixed-effects ordinal regression analysis. Comput. Methods Programs Biomed. 1996, 49:157-176.
7. Joreskog K.G., Moustaki I. Factor analysis of ordinal variables: a comparison of three approaches. Multivar. Behav. Res. 2001, 36:347-387.
8. Lee Y., Nelder J.A. Hierarchical generalized linear models. J. R. Stat. Soc. Ser. B 1996, 58:619-678.
9. Lee Y., Nelder J.A. Hierarchical generalised linear models: a synthesis of generalised linear models, random-effect models and structured dispersions. Biometrika 2001, 88:987-1006.
10. Lee Y., Nelder J.A. Conditional and marginal models: another view. Statist. Sci. 2004, 19:219-228.
11. Lee Y., Nelder J.A., Noh M. H-likelihood: problems and solutions. Stat. Comput. 2007, 17:49-55.
12. Lee Y., Nelder J.A., Pawitan Y. Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood 2006, Chapman & Hall, CRC, Boca Raton, FL.
13. Longford N.T. Logistic regression with random coefficients. Comput. Statist. Data Anal. 1994, 17:1-15.
14. McCulloch C.E. Maximum likelihood algorithms for generalized linear mixed models. J. Amer. Statist. Assoc. 1997, 92:162-170.
15. Meng X.L. Decoding the H-likelihood. Statist. Sci. 2009, 24:280-293.
16. Mislevy R.J. Recent developments in the factor analysis of categorical variables. J. Educ. Behav. Stat. 1986, 11:3-31.
17. Moustaki I., Joreskog K.G., Mavridis D. Factor models for ordinal variables with covariate effects on the manifest and latent variables: a comparison of LISREL and IRT approaches. Struct. Equ. Model. 2004, 11:487-513.
18. Naylor J.C., Smith A.F.M. Applications of a method for the efficient computation of posterior distributions. J. R. Stat. Soc. Ser. C Appl. Stat. 1982, 31:214-225.
19. Pinheiro J.C., Chao E.C. Efficient Laplacian and adaptive Gaussian quadrature algorithms for multilevel generalized linear mixed models. J. Comput. Graph. Statist. 2006, 15:58-81.
20. Raudenbush S.W., Yang M.L., Yosef M. Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate Laplace approximation. J. Comput. Graph. Statist. 2000, 9:141-157.