메뉴 건너뛰기




Volumn 6, Issue 3, 2003, Pages 309-327

How to Deal with Missing Categorical Data: Test of a Simple Bayesian Method

Author keywords

Bayesian; Categorical variable; Imputation; Missing data

Indexed keywords


EID: 0038686154     PISSN: 10944281     EISSN: None     Source Type: Journal    
DOI: 10.1177/1094428103254672     Document Type: Review
Times cited : (36)

References (28)
  • 2
    • 0035755636 scopus 로고    scopus 로고
    • A comparison of inclusive and restrictive strategies in modern missing data procedures
    • Collins, L. M., Schafer, J. L., & Kam, C. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6(4), 330-351.
    • (2001) Psychological Methods , vol.6 , Issue.4 , pp. 330-351
    • Collins, L.M.1    Schafer, J.L.2    Kam, C.3
  • 3
    • 0002629270 scopus 로고
    • Maximum likelihood estimation from incomplete data via the EM algorithm
    • Series B
    • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood estimation from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(Series B), 1-38.
    • (1977) Journal of the Royal Statistical Society , vol.39 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 4
    • 0002543932 scopus 로고    scopus 로고
    • A primer on maximum likelihood algorithms available for use with missing data
    • Enders, C. K. (2001a). A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling, 8, 128-141.
    • (2001) Structural Equation Modeling , vol.8 , pp. 128-141
    • Enders, C.K.1
  • 5
    • 0035756118 scopus 로고    scopus 로고
    • The impact of nonnormality on full information maximum likelihood estimation for structural equation models with missing data
    • Enders, C. K. (2001b). The impact of nonnormality on full information maximum likelihood estimation for structural equation models with missing data. Psychological Methods, 6(4), 352-370.
    • (2001) Psychological Methods , vol.6 , Issue.4 , pp. 352-370
    • Enders, C.K.1
  • 6
    • 0030785266 scopus 로고    scopus 로고
    • Logistic regression models with missing covariate values for complex survey data
    • Gao, S., & Hui, S. L. (1997). Logistic regression models with missing covariate values for complex survey data. Statistics in Medicine, 16, 2419-2428.
    • (1997) Statistics in Medicine , vol.16 , pp. 2419-2428
    • Gao, S.1    Hui, S.L.2
  • 8
    • 0032331359 scopus 로고    scopus 로고
    • Not asked and not answered: Multiple imputation for multiple surveys
    • Gelman, A., King, G., & Liu, C. (1998). Not asked and not answered: Multiple imputation for multiple surveys. Journal of the American Statistical Association, 93(443), 846-874.
    • (1998) Journal of the American Statistical Association , vol.93 , Issue.443 , pp. 846-874
    • Gelman, A.1    King, G.2    Liu, C.3
  • 10
    • 0003620332 scopus 로고    scopus 로고
    • Reject inference in credit operations
    • E. Mays (Ed.). Chicago: Glenlake
    • Hand, D. J. (1998). Reject inference in credit operations. In E. Mays (Ed.), Credit risk modeling: Design and application (pp. 181-190). Chicago: Glenlake.
    • (1998) Credit Risk Modeling: Design and Application , pp. 181-190
    • Hand, D.J.1
  • 11
    • 0035530907 scopus 로고    scopus 로고
    • Measuring diagnostic accuracy of statistical prediction rules
    • Hand, D. J. (2001). Measuring diagnostic accuracy of statistical prediction rules. Statistica Neerlandica, 53, 3-16.
    • (2001) Statistica Neerlandica , vol.53 , pp. 3-16
    • Hand, D.J.1
  • 14
    • 2342593951 scopus 로고    scopus 로고
    • Imputing missing values: The effect on the accuracy of classification
    • Mundform, D. J., & Whitcomb, A. (1998). Imputing missing values: The effect on the accuracy of classification. Multiple Linear Regression Viewpoints, 25, 13-19.
    • (1998) Multiple Linear Regression Viewpoints , vol.25 , pp. 13-19
    • Mundform, D.J.1    Whitcomb, A.2
  • 15
    • 1642425438 scopus 로고
    • (Discussion Paper No. 92-7). Washington, DC: U.S. Bureau of the Census, Center for Economic Studies
    • Nucci, A. R. (1992). The characteristics of business owners database (Discussion Paper No. 92-7). Washington, DC: U.S. Bureau of the Census, Center for Economic Studies.
    • (1992) The Characteristics of Business Owners Database
    • Nucci, A.R.1
  • 16
    • 0033636937 scopus 로고    scopus 로고
    • Bivariate binary data analysis with nonignorably missing outcomes
    • Paik, M. C., Sacco, R., & Lin, I.-F. (2000). Bivariate binary data analysis with nonignorably missing outcomes. Biometrics, 56, 1145-1156.
    • (2000) Biometrics , vol.56 , pp. 1145-1156
    • Paik, M.C.1    Sacco, R.2    Lin, I.-F.3
  • 17
    • 21844483562 scopus 로고
    • Missing data: A conceptual review for applied psychologists
    • Roth, P. (1994). Missing data: A conceptual review for applied psychologists. Personnel Psychology, 47, 537-560.
    • (1994) Personnel Psychology , vol.47 , pp. 537-560
    • Roth, P.1
  • 18
    • 0001523948 scopus 로고
    • A Monte Carlo analysis of missing data techniques in a HRM setting
    • Roth, P. L., & Switzer, F. S. (1995). A Monte Carlo analysis of missing data techniques in a HRM setting. Journal of Management, 21(5), 1003-1023.
    • (1995) Journal of Management , vol.21 , Issue.5 , pp. 1003-1023
    • Roth, P.L.1    Switzer, F.S.2
  • 19
    • 0017133178 scopus 로고
    • Inference and missing data
    • Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581-592.
    • (1976) Biometrika , vol.63 , pp. 581-592
    • Rubin, D.B.1
  • 24
    • 85047673373 scopus 로고    scopus 로고
    • Missing data: Our view of the state of the art
    • Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147-177.
    • (2002) Psychological Methods , vol.7 , Issue.2 , pp. 147-177
    • Schafer, J.L.1    Graham, J.W.2
  • 25
    • 0035748192 scopus 로고    scopus 로고
    • The use of multiple imputation for the analysis of missing data
    • Sinharay, S., Stern, H. S., & Russell, D. (2001). The use of multiple imputation for the analysis of missing data. Psychological Methods, 6(4), 317-329.
    • (2001) Psychological Methods , vol.6 , Issue.4 , pp. 317-329
    • Sinharay, S.1    Stern, H.S.2    Russell, D.3
  • 26
    • 0032342705 scopus 로고    scopus 로고
    • Systematic data loss in HRM settings: A Monte Carlo analysis
    • Switzer, F. S., III, Roth, P. L., & Switzer, D. M. (1998). Systematic data loss in HRM settings: A Monte Carlo analysis. Journal of Management, 24(6), 763-779.
    • (1998) Journal of Management , vol.24 , Issue.6 , pp. 763-779
    • Switzer III, F.S.1    Roth, P.L.2    Switzer, D.M.3
  • 27
    • 0030775537 scopus 로고    scopus 로고
    • Weighted estimating equations with nonignorably missing response data
    • Troxel, A. B., Lipsitz, S. R., & Brennan, T. A. (1997). Weighted estimating equations with nonignorably missing response data. Biometrics, 53, 857-869.
    • (1997) Biometrics , vol.53 , pp. 857-869
    • Troxel, A.B.1    Lipsitz, S.R.2    Brennan, T.A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.