메뉴 건너뛰기




Volumn 39, Issue 10, 2012, Pages 2177-2198

Multiple imputation using multivariate gh transformations

Author keywords

bootstrap; hospital quality; imputation diagnostics; latent variable; multivariate missingness; quantiles

Indexed keywords


EID: 84865806474     PISSN: 02664763     EISSN: 13600532     Source Type: Journal    
DOI: 10.1080/02664763.2012.702268     Document Type: Article
Times cited : (12)

References (40)
  • 2
    • 59849109653 scopus 로고    scopus 로고
    • Bayesian estimation of quantile distributions
    • Allingham, D., King, R. A.R. and Mengersen, K. L. 2009. Bayesian estimation of quantile distributions. Stat. Comput., 19: 189-201.
    • (2009) Stat. Comput. , vol.19 , pp. 189-201
    • Allingham, D.1    King, R.A.R.2    Mengersen, K.L.3
  • 3
    • 34347407592 scopus 로고    scopus 로고
    • Multiple imputation of discrete and continuous data by fully conditional specification
    • van Buuren, S. 2007. Multiple imputation of discrete and continuous data by fully conditional specification. Stat. Methods Med. Res., 16: 219-242.
    • (2007) Stat. Methods Med. Res. , vol.16 , pp. 219-242
    • van Buuren, S.1
  • 4
    • 60749102392 scopus 로고    scopus 로고
    • Multiple imputation under the generalized lambda distribution
    • Demirtas, H. 2008. Multiple imputation under the generalized lambda distribution. J. Biopharm. Statist., 19: 77-89.
    • (2008) J. Biopharm. Statist. , vol.19 , pp. 77-89
    • Demirtas, H.1
  • 5
    • 38349186156 scopus 로고    scopus 로고
    • Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: A simulation assessment
    • Demirtas, H., Freels, S. A. and Yucel, R. M. 2008. Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: A simulation assessment. J. Stat. Comput. Simul., 78: 69-84.
    • (2008) J. Stat. Comput. Simul. , vol.78 , pp. 69-84
    • Demirtas, H.1    Freels, S.A.2    Yucel, R.M.3
  • 6
    • 33750929708 scopus 로고    scopus 로고
    • Comment to 'Tukey's gh distribution for multiple imputation' by He and Raghunathan
    • Demirtas, H. and Hedeker, D. 2006. Comment to 'Tukey's gh distribution for multiple imputation' by He and Raghunathan. Amer. Statist., 60: 348
    • (2006) Amer. Statist. , vol.60 , pp. 348
    • Demirtas, H.1    Hedeker, D.2
  • 7
    • 41549095587 scopus 로고    scopus 로고
    • Imputing continuous data under some non-Gaussian distributions
    • Demirtas, H. and Hedeker, D. 2008. Imputing continuous data under some non-Gaussian distributions. Statist. Neerlandica, 62: 193-205.
    • (2008) Statist. Neerlandica , vol.62 , pp. 193-205
    • Demirtas, H.1    Hedeker, D.2
  • 9
    • 5044252133 scopus 로고    scopus 로고
    • Large wind speeds: Modeling and outlier detection
    • Dupuis, D. J. and Field, C. A. 2003. Large wind speeds: Modeling and outlier detection. J. Agric. Biol. Environ. Stat., 9: 105-121.
    • (2003) J. Agric. Biol. Environ. Stat. , vol.9 , pp. 105-121
    • Dupuis, D.J.1    Field, C.A.2
  • 11
    • 1542378393 scopus 로고    scopus 로고
    • Using the gh distribution to model extreme wind speeds
    • Field, C. A. 2004. Using the gh distribution to model extreme wind speeds. J. Statist. Plann. Inference, 122: 15-22.
    • (2004) J. Statist. Plann. Inference , vol.122 , pp. 15-22
    • Field, C.A.1
  • 12
    • 33344464406 scopus 로고    scopus 로고
    • Multivariate g-and-h distribution
    • Field, C. A. and Genton, M. G. 2006. Multivariate g-and-h distribution. Technometrics, 48: 104-111.
    • (2006) Technometrics , vol.48 , pp. 104-111
    • Field, C.A.1    Genton, M.G.2
  • 13
    • 70350678979 scopus 로고    scopus 로고
    • Generalized Tukey-type distributions with application to financial and teletraffic data
    • Fischer, M. 2010. Generalized Tukey-type distributions with application to financial and teletraffic data. Statist. Papers, 51: 41-56.
    • (2010) Statist. Papers , vol.51 , pp. 41-56
    • Fischer, M.1
  • 14
    • 33847251099 scopus 로고    scopus 로고
    • Tukey-type distribution in the context of financial data
    • Fischer, M., Horn, A. and Klein, A. 2007. Tukey-type distribution in the context of financial data. Comm. Statist. Theory Methods, 36: 23-35.
    • (2007) Comm. Statist. Theory Methods , vol.36 , pp. 23-35
    • Fischer, M.1    Horn, A.2    Klein, A.3
  • 15
    • 84950453304 scopus 로고
    • Sampling-based approaches to calculating marginal densities
    • Gelfand, A. E. and Smith, A. F.M. 1990. Sampling-based approaches to calculating marginal densities. J. Amer. Statist. Assoc., 85: 398-409.
    • (1990) J. Amer. Statist. Assoc. , vol.85 , pp. 398-409
    • Gelfand, A.E.1    Smith, A.F.M.2
  • 16
    • 15044358532 scopus 로고    scopus 로고
    • Multiple imputation for model checking: Completed-data plots with missing and latent data
    • Gelman, A. E., Mechelen, I. V., Verbeke, G., Heitjan, D. F. and Meulders, M. 2005. Multiple imputation for model checking: Completed-data plots with missing and latent data. Biometrics, 61: 74-85.
    • (2005) Biometrics , vol.61 , pp. 74-85
    • Gelman, A.E.1    Mechelen, I.V.2    Verbeke, G.3    Heitjan, D.F.4    Meulders, M.5
  • 17
    • 84972492387 scopus 로고
    • Inference from iterative simulation using multiple sequences (with discussion)
    • Gelman, A. E. and Rubin, D. B. 1992. Inference from iterative simulation using multiple sequences (with discussion). Statist. Sci., 7: 457-511.
    • (1992) Statist. Sci. , vol.7 , pp. 457-511
    • Gelman, A.E.1    Rubin, D.B.2
  • 19
    • 34250686456 scopus 로고    scopus 로고
    • Multiple imputation: Review of theory, implementation, and software
    • Harel, O. and Zhou, X. H. 2007. Multiple imputation: Review of theory, implementation, and software. Stat. Med., 26: 3057-3077.
    • (2007) Stat. Med. , vol.26 , pp. 3057-3077
    • Harel, O.1    Zhou, X.H.2
  • 21
    • 33746050373 scopus 로고    scopus 로고
    • Bayesian estimation of g-and-k distributions using MCMC
    • Haynes, M. and Mengersen, K. 2005. Bayesian estimation of g-and-k distributions using MCMC. Comput. Statist., 20: 7-30.
    • (2005) Comput. Statist. , vol.20 , pp. 7-30
    • Haynes, M.1    Mengersen, K.2
  • 22
    • 53249156451 scopus 로고    scopus 로고
    • Generalized control charts for nonnormal data using g-and-k distributions
    • Haynes, M., Mengersen, K. and Rippon, P. 2008. Generalized control charts for nonnormal data using g-and-k distributions. Comm. Statist. Simulation Comput., 37: 1881-1903.
    • (2008) Comm. Statist. Simulation Comput. , vol.37 , pp. 1881-1903
    • Haynes, M.1    Mengersen, K.2    Rippon, P.3
  • 23
    • 33747517213 scopus 로고    scopus 로고
    • Tukey's gh distribution for multiple imputation
    • He, Y. and Raghunathan, T. E. 2006. Tukey's gh distribution for multiple imputation. Amer. Statist., 60: 251-256.
    • (2006) Amer. Statist. , vol.60 , pp. 251-256
    • He, Y.1    Raghunathan, T.E.2
  • 24
    • 84865832802 scopus 로고    scopus 로고
    • On the performance of sequential regression multiple imputation methods with non normal error distributions
    • He, Y. and Raghunathan, T. E. 2009. On the performance of sequential regression multiple imputation methods with non normal error distributions. Comm. Statist. Simulation Comput., 38: 856-883.
    • (2009) Comm. Statist. Simulation Comput. , vol.38 , pp. 856-883
    • He, Y.1    Raghunathan, T.E.2
  • 25
    • 0002449934 scopus 로고
    • Summarizing shape numerically: The g-and-h distributions
    • In: Hoaglin D. C., Mosteller F., Tukey J. W., editors New York: Wiley
    • Hoaglin, D. C. 1985. "Summarizing shape numerically: The g-and-h distributions". In Exploring Data Tables, Trends, and Shapes, Edited by: Hoaglin, D. C., Mosteller, F. and Tukey, J. W. 461-513. New York: Wiley.
    • (1985) Exploring Data Tables, Trends, and Shapes , pp. 461-513
    • Hoaglin, D.C.1
  • 26
    • 8644254410 scopus 로고    scopus 로고
    • Robust likelihood-based analysis of multivariate data with missing values
    • Little, R. J.A. and An, H. 2004. Robust likelihood-based analysis of multivariate data with missing values. Statist. Sinica, 14: 949-968.
    • (2004) Statist. Sinica , vol.14 , pp. 949-968
    • Little, R.J.A.1    An, H.2
  • 28
    • 0000471962 scopus 로고
    • Some properties of the Tukey g and h family of distributions
    • Martinez, J. and Iglewicz, B. 1984. Some properties of the Tukey g and h family of distributions. Comm. Statist. Theory Methods, 13: 353-369.
    • (1984) Comm. Statist. Theory Methods , vol.13 , pp. 353-369
    • Martinez, J.1    Iglewicz, B.2
  • 29
    • 0000978375 scopus 로고
    • Modeling skewness and kurtosis in the London stock exchange FT-SE index return distributions
    • Mills, T. C. 1995. Modeling skewness and kurtosis in the London stock exchange FT-SE index return distributions. Statistician, 44: 323-332.
    • (1995) Statistician , vol.44 , pp. 323-332
    • Mills, T.C.1
  • 31
    • 0002344593 scopus 로고    scopus 로고
    • A multivariate technique for multiply imputing missing values using a sequence of regression models
    • Raghunathan, T. E., Lepkowski, J. M., VanHoewyk, J. and Solenberger, P. 2001. A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv. Methodol., 27: 85-95.
    • (2001) Surv. Methodol. , vol.27 , pp. 85-95
    • Raghunathan, T.E.1    Lepkowski, J.M.2    VanHoewyk, J.3    Solenberger, P.4
  • 32
    • 0141791122 scopus 로고    scopus 로고
    • Numerical maximum likelihood estimation for the g-and-k and generalized g-and-h distributions
    • Rayner, G. and MacGillivary, H. 2002. Numerical maximum likelihood estimation for the g-and-k and generalized g-and-h distributions. Stat. Comput., 12: 55-75.
    • (2002) Stat. Comput. , vol.12 , pp. 55-75
    • Rayner, G.1    MacGillivary, H.2
  • 33
    • 26444574824 scopus 로고    scopus 로고
    • Early experience with pay-for-performance, from concept to practice
    • Rosenthal, M. B., Frank, R. G., Zhonghe, L. and Epstein, A. M. 2005. Early experience with pay-for-performance, from concept to practice. J. Am. Med. Assoc., 294: 1788-1793.
    • (2005) J. Am. Med. Assoc. , vol.294 , pp. 1788-1793
    • Rosenthal, M.B.1    Frank, R.G.2    Zhonghe, L.3    Epstein, A.M.4
  • 34
    • 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
  • 37
    • 84950758368 scopus 로고
    • The calculation of posterior distributions by data augmentation (with discussion)
    • Tanner, M. A. and Wong, W. H. 1987. The calculation of posterior distributions by data augmentation (with discussion). J. Amer. Statist. Assoc., 82: 528-550.
    • (1987) J. Amer. Statist. Assoc. , vol.82 , pp. 528-550
    • Tanner, M.A.1    Wong, W.H.2
  • 38
    • 84865832204 scopus 로고    scopus 로고
    • North Dartmouth, MA: NSF-sponsored regional research conference at Southeastern Massachusetts University
    • Tukey, J. W. Modern techniques in data analysis. North Dartmouth, MA: NSF-sponsored regional research conference at Southeastern Massachusetts University.
    • Modern techniques in data analysis
    • Tukey, J.W.1
  • 39
    • 38249002469 scopus 로고
    • A note on the multivariate Box-Cox transformation to normality
    • Velilla, S. 1993. A note on the multivariate Box-Cox transformation to normality. Statist. Probab. Lett., 17: 259-263.
    • (1993) Statist. Probab. Lett. , vol.17 , pp. 259-263
    • Velilla, S.1
  • 40
    • 81955167495 scopus 로고    scopus 로고
    • Imputation of categorical variables using Gaussian-based routines
    • Yucel, R., He, Y. and Zaslavsky, A. M. 2011. Imputation of categorical variables using Gaussian-based routines. Stat. Med., 30: 3447-3460.
    • (2011) Stat. Med. , vol.30 , pp. 3447-3460
    • Yucel, R.1    He, Y.2    Zaslavsky, A.M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.