메뉴 건너뛰기




Volumn 52, Issue 12, 2008, Pages 5066-5074

Accuracy of Laplace approximation for discrete response mixed models

Author keywords

[No Author keywords available]

Indexed keywords

ASYMPTOTIC ANALYSIS; COMPUTATIONAL METHODS; ESTIMATION; MAXIMUM LIKELIHOOD ESTIMATION; POLYNOMIAL APPROXIMATION; RANDOM PROCESSES;

EID: 47749090885     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2008.05.002     Document Type: Article
Times cited : (118)

References (16)
  • 1
    • 0003020981 scopus 로고
    • Bias correction in generalized linear mixed models with a single component of dispersion
    • Breslow N.E., and Lin X. Bias correction in generalized linear mixed models with a single component of dispersion. Biometrika 82 (1995) 81-91
    • (1995) Biometrika , vol.82 , pp. 81-91
    • Breslow, N.E.1    Lin, X.2
  • 2
    • 0036750728 scopus 로고    scopus 로고
    • Using spherical-radial quadrature to fit generalized linear mixed effects models
    • Clarkson D.B., and Zhan Y. Using spherical-radial quadrature to fit generalized linear mixed effects models. J. Comput. Graph. Statist. 11 (2002) 639-659
    • (2002) J. Comput. Graph. Statist. , vol.11 , pp. 639-659
    • Clarkson, D.B.1    Zhan, Y.2
  • 3
    • 5444232913 scopus 로고    scopus 로고
    • Nonlinear models for repeated measurements: An overview and update
    • Davidian M., and Giltinan D.M. Nonlinear models for repeated measurements: An overview and update. J. Agric. Biol. Environ. Stat. 8 (2003) 387-419
    • (2003) J. Agric. Biol. Environ. Stat. , vol.8 , pp. 387-419
    • Davidian, M.1    Giltinan, D.M.2
  • 5
    • 0032386920 scopus 로고    scopus 로고
    • A simple illustration of the failure of PQL, IRREML, and APHL as approximate ML methods for mixed models for binary data
    • Engel B. A simple illustration of the failure of PQL, IRREML, and APHL as approximate ML methods for mixed models for binary data. Biom. J. 40 (1998) 141-154
    • (1998) Biom. J. , vol.40 , pp. 141-154
    • Engel, B.1
  • 7
    • 0000312997 scopus 로고    scopus 로고
    • Hierarchical generalized linear models (with discussion)
    • Lee Y., and Nelder J.A. Hierarchical generalized linear models (with discussion). J. Roy. Statist. Soc. B 58 (1996) 619-678
    • (1996) J. Roy. Statist. Soc. B , vol.58 , pp. 619-678
    • Lee, Y.1    Nelder, J.A.2
  • 11
    • 33947127673 scopus 로고    scopus 로고
    • REML estimation for binary data in GLMMs
    • Noh M., and Lee Y. REML estimation for binary data in GLMMs. J. Multivariate Anal. 98 (2007) 896-915
    • (2007) J. Multivariate Anal. , vol.98 , pp. 896-915
    • Noh, M.1    Lee, Y.2
  • 12
    • 84946045727 scopus 로고
    • Approximations to the log-likelihood function in the nonlinear mixed-effects model
    • Pinheiro J.C., and Bates D.M. Approximations to the log-likelihood function in the nonlinear mixed-effects model. J. Comput. Graph. Statist. 4 (1995) 12-35
    • (1995) J. Comput. Graph. Statist. , vol.4 , pp. 12-35
    • Pinheiro, J.C.1    Bates, D.M.2
  • 13
    • 33645565987 scopus 로고    scopus 로고
    • Efficient Laplacian and adaptive Gaussian quadrature algorithms for multilevel generalized linear mixed models
    • Pinheiro J.C., and Chao E.C. Efficient Laplacian and adaptive Gaussian quadrature algorithms for multilevel generalized linear mixed models. J. Comput. Graph. Statist. 15 (2006) 58-81
    • (2006) J. Comput. Graph. Statist. , vol.15 , pp. 58-81
    • Pinheiro, J.C.1    Chao, E.C.2
  • 14
    • 77950035264 scopus 로고    scopus 로고
    • Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate Laplace approximation
    • Raudenbush S.W., Yang M.L., and Yosef M. Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate Laplace approximation. J. Comput. Graph. Statist. 9 (2000) 141-157
    • (2000) J. Comput. Graph. Statist. , vol.9 , pp. 141-157
    • Raudenbush, S.W.1    Yang, M.L.2    Yosef, M.3
  • 15
    • 0344081997 scopus 로고    scopus 로고
    • A pairwise likelihood approach to estimation in multilevel probit models
    • Renard D., Molenberghs G., and Geys H. A pairwise likelihood approach to estimation in multilevel probit models. Comput. Statist. Data Anal. 44 (2004) 649-667
    • (2004) Comput. Statist. Data Anal. , vol.44 , pp. 649-667
    • Renard, D.1    Molenberghs, G.2    Geys, H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.