메뉴 건너뛰기




Volumn 83, Issue 1, 2013, Pages 25-36

Nesting monte carlo EM for high-dimensional item factor analysis

Author keywords

Full information item factor analysis; Monte Carlo EM; Nesting EM

Indexed keywords


EID: 84901688271     PISSN: 00949655     EISSN: 15635163     Source Type: Journal    
DOI: 10.1080/00949655.2011.599810     Document Type: Article
Times cited : (1)

References (27)
  • 3
    • 0034267917 scopus 로고    scopus 로고
    • A discussion of item response theory and its application in health status assessment
    • [3] F. Cella and C.H. Chang, A discussion of item response theory and its application in health status assessment, Med. Care 38(Suppl. II) (2000), pp. 66–72.
    • (2000) Med. Care , vol.38 , pp. 66-72
    • Cella, F.1    Chang, C.H.2
  • 4
    • 34447136274 scopus 로고    scopus 로고
    • Next steps for use of item response theory in the assessment of health outcomes
    • [4] R. Hays and J. Lipscomb, Next steps for use of item response theory in the assessment of health outcomes, Qual. Life Res. 16 (2007), pp. 195–199.
    • (2007) Qual. Life Res , vol.16 , pp. 195-199
    • Hays, R.1    Lipscomb, J.2
  • 5
    • 34247399601 scopus 로고    scopus 로고
    • Item response theory analyses of physical functioning items in the medical outcome study
    • [5] R. Hays, H. Liu, K. Spritzer, and D. Cella, Item response theory analyses of physical functioning items in the medical outcome study, Med. Care 45 (2007), pp. 32–38.
    • (2007) Med. Care , vol.45 , pp. 32-38
    • Hays, R.1    Liu, H.2    Spritzer, K.3    Cella, D.4
  • 6
    • 11344280118 scopus 로고    scopus 로고
    • Detection of determinant genes and diagnostic via item response theory
    • [6] H. Tavares, D. Andrade, and C. Pereira, Detection of determinant genes and diagnostic via item response theory, Genet. Mol. Biol. 27 (2004), pp. 679–685.
    • (2004) Genet. Mol. Biol , vol.27 , pp. 679-685
    • Tavares, H.1    Andrade, D.2    Pereira, C.3
  • 7
    • 36749057742 scopus 로고    scopus 로고
    • Penalized item response theory models: Application to epigenetic alterations in bladder cancer
    • [7] E. Houseman, C. Marsit, M. Karagas, and L. Ryan, Penalized item response theory models: Application to epigenetic alterations in bladder cancer, Biometrics 63 (2007), pp. 1269–1277.
    • (2007) Biometrics , vol.63 , pp. 1269-1277
    • Houseman, E.1    Marsit, C.2    Karagas, M.3    Ryan, L.4
  • 8
    • 42349104040 scopus 로고    scopus 로고
    • Application of multidimensional selective item response regression model for studying multiple gene methylation in SV40 oncogenic pathways
    • [8] H. Lin, Z. Feng, Y. Yu, Y. Zheng, N. Shivapurkar, and A. Gazdar, Application of multidimensional selective item response regression model for studying multiple gene methylation in SV40 oncogenic pathways, J. Amer. Statist. Assoc. 103 (2008), pp. 201–211.
    • (2008) J. Amer. Statist. Assoc , vol.103 , pp. 201-211
    • Lin, H.1    Feng, Z.2    Yu, Y.3    Zheng, Y.4    Shivapurkar, N.5    Gazdar, A.6
  • 9
    • 26944443048 scopus 로고    scopus 로고
    • Semantic analysis of association rules via item response theory
    • [9] S. Hamano and M. Sato, Semantic analysis of association rules via item response theory, Mach. Learn. Data Mining Pattern Recogn. 3587 (2005), pp. 641–650.
    • (2005) Mach. Learn. Data Mining Pattern Recogn , vol.3587 , pp. 641-650
    • Hamano, S.1    Sato, M.2
  • 10
    • 69949111872 scopus 로고    scopus 로고
    • Application of item response theory to collaborative filtering
    • ISNN 2009
    • [10] B. Hu, Y. Zhou, J. Wang, L. Li, and L. Shen, Application of item response theory to collaborative filtering, Adv. Neural Networks–ISNN 2009 (2009), pp. 766–773.
    • (2009) Adv. Neural Networks , pp. 766-773
    • Hu, B.1    Zhou, Y.2    Wang, J.3    Li, L.4    Shen, L.5
  • 11
    • 0000433590 scopus 로고
    • Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm
    • [11] R.D. Bock and M. Aitkin, Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm, Psychometrika 46 (1981), pp. 443–459.
    • (1981) Psychometrika , vol.46 , pp. 443-459
    • Bock, R.D.1    Aitkin, M.2
  • 13
    • 28844454343 scopus 로고    scopus 로고
    • High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature
    • [13] S. Schilling and R.D. Bock, High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature, Psychometrika 70 (2005), pp. 533–555.
    • (2005) Psychometrika , vol.70 , pp. 533-555
    • Schilling, S.1    Bock, R.D.2
  • 14
    • 0030336223 scopus 로고    scopus 로고
    • Fitting full-information item factor models and an empirical investigation of bridge sampling
    • [14] X.L. Meng and S. Schilling, Fitting full-information item factor models and an empirical investigation of bridge sampling, J. Amer. Statist. Assoc. 91 (1996), pp. 1254–1267.
    • (1996) J. Amer. Statist. Assoc , vol.91 , pp. 1254-1267
    • Meng, X.L.1    Schilling, S.2
  • 15
    • 77950076362 scopus 로고    scopus 로고
    • High-dimensional exploratory item factor analysis by a Metropolis–Hastings Robbins–Monro algorithm
    • [15] L. Cai, High-dimensional exploratory item factor analysis by a Metropolis–Hastings Robbins–Monro algorithm, Psychometrika 75 (2010), pp. 33–57.
    • (2010) Psychometrika , vol.75 , pp. 33-57
    • Cai, L.1
  • 16
    • 0001303107 scopus 로고
    • A note on Gauss–Hermite quadrature
    • [16] Q. Liu and D. Pierce, A note on Gauss–Hermite quadrature, Biometrika 81 (1994), p. 624.
    • (1994) Biometrika , vol.81 , pp. 624
    • Liu, Q.1    Pierce, D.2
  • 17
    • 84993741970 scopus 로고    scopus 로고
    • A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical model
    • [17] J. Booth, J. Hobert, and W. Jank, A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical model, Statist. Model. 1 (2001), p. 333.
    • (2001) Statist. Model , vol.1 , pp. 333
    • Booth, J.1    Hobert, J.2    Jank, W.3
  • 19
    • 0034420823 scopus 로고    scopus 로고
    • Nesting EM algorithms for computational efficiency
    • [19] D.A. van Dyk, Nesting EM algorithms for computational efficiency, Statistica Sinica 10 (2000), pp. 203–225.
    • (2000) Statistica Sinica , vol.10 , pp. 203-225
    • Van Dyk, D.A.1
  • 20
    • 6344222582 scopus 로고    scopus 로고
    • Factor rotations in factor analyses
    • M. Lewis-Beck, A. Bryman, and T. Futing, eds., Sage, Thousand Oaks, CA
    • [20] H. Abdi, Factor rotations in factor analyses, in Encyclopedia for Research Methods for the Social Sciences, M. Lewis-Beck, A. Bryman, and T. Futing, eds., Sage, Thousand Oaks, CA, 2003, pp. 792–795.
    • (2003) Encyclopedia for Research Methods for the Social Sciences , pp. 792-795
    • Abdi, H.1
  • 21
    • 0032375617 scopus 로고    scopus 로고
    • Analysis of two-level structural equation models via EM type algorithms
    • [21] S.Y. Lee and W.Y. Poon, Analysis of two-level structural equation models via EM type algorithms, Statistica Sinica 8 (1998), pp. 749–766.
    • (1998) Statistica Sinica , vol.8 , pp. 749-766
    • Lee, S.Y.1    Poon, W.Y.2
  • 22
    • 0031479575 scopus 로고    scopus 로고
    • Maximum likelihood algorithms for generalized linear mixed models
    • [22] C.E. McCulloch, Maximum likelihood algorithms for generalized linear mixed models, J. Amer. Statist. Assoc. 92 (1997), pp. 162–170.
    • (1997) J. Amer. Statist. Assoc , vol.92 , pp. 162-170
    • McCulloch, C.E.1
  • 23
    • 27744588060 scopus 로고    scopus 로고
    • Generalized linear latent variable models for repeated measures of spatially correlated multivariate data
    • [23] J. Zhu, J.C. Eickhoff, and P. Yan, Generalized linear latent variable models for repeated measures of spatially correlated multivariate data, Biometrics 61 (2005), pp. 674–683.
    • (2005) Biometrics , vol.61 , pp. 674-683
    • Zhu, J.1    Eickhoff, J.C.2    Yan, P.3
  • 24
    • 84916537550 scopus 로고
    • Bayesian analysis of binary and polychotomous response data
    • [24] J.H. Albert and S. Chib, Bayesian analysis of binary and polychotomous response data, J. Amer. Statist. Assoc. 88 (1993), pp. 669–679.
    • (1993) J. Amer. Statist. Assoc , vol.88 , pp. 669-679
    • Albert, J.H.1    Chib, S.2
  • 25
    • 34250232348 scopus 로고
    • EM algorithms for ML factor analysis
    • [25] D. Rubin and D. Thayer, EM algorithms for ML factor analysis, Psychometrika 47 (1982), pp. 69–76.
    • (1982) Psychometrika , vol.47 , pp. 69-76
    • Rubin, D.1    Thayer, D.2
  • 26
    • 0000251971 scopus 로고
    • Maximum likelihood estimation via the ECM algorithm: A general framework
    • [26] X.L. Meng and D.B. Rubin, Maximum likelihood estimation via the ECM algorithm: A general framework, Biometrika 80 (1993), pp. 267–278.
    • (1993) Biometrika , vol.80 , pp. 267-278
    • Meng, X.L.1    Rubin, D.B.2
  • 27
    • 0001508169 scopus 로고    scopus 로고
    • Parameter expansion to accelerate EM: The PX-EM algorithm
    • [27] C.H. Liu, D.B. Rubin, and Y.N. Wu, Parameter expansion to accelerate EM: The PX-EM algorithm, Biometrika 85 (1998), pp. 755–770.
    • (1998) Biometrika , vol.85 , pp. 755-770
    • Liu, C.H.1    Rubin, D.B.2    Wu, Y.N.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.