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Using a mixture model for multiple imputation in the presence of outliers: the ‘Healthy for life’ project
dc.contributor.authorElliott, Michael R.en_US
dc.contributor.authorStettler, Nicolasen_US
dc.date.accessioned2010-06-01T21:48:19Z
dc.date.available2010-06-01T21:48:19Z
dc.date.issued2007-01en_US
dc.identifier.citationElliott, Michael R.; Stettler, Nicolas (2007). "Using a mixture model for multiple imputation in the presence of outliers: the ‘Healthy for life’ project." Journal of the Royal Statistical Society: Series C (Applied Statistics) 56(1): 63-78. <http://hdl.handle.net/2027.42/74845>en_US
dc.identifier.issn0035-9254en_US
dc.identifier.issn1467-9876en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/74845
dc.format.extent665071 bytes
dc.format.extent3109 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherBlackwell Publishing Ltden_US
dc.rights2007 Royal Statistical Societyen_US
dc.subject.otherBody Mass Indexen_US
dc.subject.otherChilden_US
dc.subject.otherCommunity Health Centreen_US
dc.subject.otherLatent Classen_US
dc.subject.otherMultiple-edit–Multiple-imputation Modelen_US
dc.subject.otherObesityen_US
dc.subject.otherSurvey Samplingen_US
dc.titleUsing a mixture model for multiple imputation in the presence of outliers: the ‘Healthy for life’ projecten_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumUniversity of Michigan, Ann Arbor, USAen_US
dc.contributor.affiliationotherChildren's Hospital of Philadelphia, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/74845/1/j.1467-9876.2007.00565.x.pdf
dc.identifier.doi10.1111/j.1467-9876.2007.00565.xen_US
dc.identifier.sourceJournal of the Royal Statistical Society: Series C (Applied Statistics)en_US
dc.identifier.citedreferenceAkaike, H. ( 1978 ) A Baysian analysis of the minimum AIC procedure. Ann. Inst. Statist. Math., 30, 9 – 14.en_US
dc.identifier.citedreferenceBarnett, V. and Lewis, T. ( 1994 ) Outliers in Statistical Data, 3rd edn. New York: Wiley.en_US
dc.identifier.citedreferenceBayarri, M. J. and Morales, J. ( 2003 ) Bayesian measures of surprise for outlier detection. J. Statist. Planng Inf., 111, 3 – 22.en_US
dc.identifier.citedreferenceBox, G. E. P. and Cox, D. R. ( 1964 ) An analysis of transformations (with discussion). J. R. Statist. Soc. B, 26, 211 – 252.en_US
dc.identifier.citedreferenceCampbell, N. A. ( 1980 ) Robert procedures in multivariate data analysis: I, robust covariance estimation. Appl. Statist., 29, 231 – 237.en_US
dc.identifier.citedreferenceCarlin, B. P. and Chib, S. ( 1995 ) Bayesian model choice via Markov chain Monte Carlo methods. J. R. Statist. Soc. B, 57, 473 – 484.en_US
dc.identifier.citedreferenceChaloner, K. and Brant, R. ( 1988 ) A Bayesian approach to outlier detection and residual analysis. Biometrika, 75, 651 – 659.en_US
dc.identifier.citedreferenceCole, T. J. ( 1990 ) The LMS method for constructing normalized growth standards. Eur. J. Clin. Nutrn, 44, 45 – 60.en_US
dc.identifier.citedreferenceCole, T. J. ( 1994 ) Growth charts for both cross-sectional and longitudinal data. Statist. Med., 13, 2477 – 2492.en_US
dc.identifier.citedreferenceCole, T. J., Bellizzi, M. C., Flegal, K. M. and Dietz, W. H. ( 2000 ) Establishing a standard definition for child overweight and obesity worldwide: international survey. Br. Med. J., 320, 1240 – 1245.en_US
dc.identifier.citedreferenceDempster, A. P., Laird, N. M. and Rubin, D. B. ( 1977 ) Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. R. Statist. Soc. B, 39, 1 – 38.en_US
dc.identifier.citedreferenceElliott, M. R. ( 2006 ) Multiple imputation in the presence of outliers. Technical Report 06-59. Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor.en_US
dc.identifier.citedreferenceGelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. ( 2004 ) Bayesian Data Analysis, 2nd edn. Boca Raton: Chapman and Hall–CRC.en_US
dc.identifier.citedreferenceGelman, A., Meng, X.-L. and Stern, H. S. ( 1996 ) Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statist. Sin., 6, 733 – 807.en_US
dc.identifier.citedreferenceGhosh-Dastidar, M. and Schafer, J. L. ( 2003 ) Multiple edit multiple imputation for multivariate continuous data. J. Am. Statist. Ass., 98, 807 – 817.en_US
dc.identifier.citedreferenceGreen, P. J. ( 1995 ) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82, 711 – 732.en_US
dc.identifier.citedreferenceHadi, A. S. ( 1992 ) Identifying multiple outliers in multivariate data. J. R. Statist. Soc. B, 54, 761 – 771.en_US
dc.identifier.citedreferenceHawkins, D. M. ( 1980 ) Identification of Outliers. London: Chapman and Hall.en_US
dc.identifier.citedreferenceHedley, A. A., Ogden, C. L., Johnson, C. L., Carroll, M. D., Curtin, L. R. and Flegal, K. M. ( 2004 ) Prevalence of overweight and obesity among US children, adolescents, and adults. J. Am. Med. Ass., 291, 2847 – 2850.en_US
dc.identifier.citedreferenceKimm, S. Y. and Obarzanek, E. ( 2002 ) Childhood obesity: a new pandemic of the new millennium. Pediatrics, 110, 1003 – 1007.en_US
dc.identifier.citedreferenceKoplan, J. P., Liverman, C. T. and Kraak, V. A. ( eds ) ( 2004 ) Preventing Childhood Obesity: Health in the Balance. Washington DC: National Academies Press.en_US
dc.identifier.citedreferenceKuczmarski, R. J., Ogden, C. L., Grummer-Strawn, L. M., Flegal, K. M., Guo, S. S., Wei, R., Mei, Z., Curtin, L. R., Roche, A. F. and Johnson, C. L. ( 2000 ) CDC growth charts: United States. In Advance Data from Vital and Health Statistics, no. 314. Hyattsville: National Center for Health Statistics.en_US
dc.identifier.citedreferenceLeonard, M. B., Feldman, H. I., Shults, J., Zemel, B., Foster, B. J. and Stallings, V. A. ( 2004 ) Long-term, high-dose glucocorticoids and bone mineral content in childhood glucocorticoid-sensitive nephrotic syndrome. New Engl. J. Med., 351, 868 – 875.en_US
dc.identifier.citedreferenceLi, K. H. ( 1988 ) Imputation using Markov Chains. J. Statist. Computn Simuln, 30, 57 – 79.en_US
dc.identifier.citedreferenceLittle, R. J. A. ( 1988 ) Robust estimation of the mean and covariance matrix from data with missing values. Appl. Statist., 37, 23 – 38.en_US
dc.identifier.citedreferenceLittle, R. J. A. and Smith, P. J. ( 1987 ) Editing and imputation for quantitative survey data. J. Am. Statist. Ass., 82, 58 – 68.en_US
dc.identifier.citedreferenceMeng, X. L. ( 1994 ) Multiple imputation inferences with uncongenial sources of input (with discussion). Statist. Sci., 9, 538 – 573.en_US
dc.identifier.citedreferenceOgden, C. L., Flegal, K. M., Carroll, M. D. and Johnson, C. L. ( 2002 ) Prevalence and trends in overweight among US children and adolescents, 1999-2000. J. Am. Med. Ass., 288, 1728 – 1732.en_US
dc.identifier.citedreferencePatterson, B. H., Dayton, C. M. and Graubard, B. I. ( 2002 ) Latent class analysis of complex sample survey data: application to dietary data (with discussion). J. Am. Statist. Ass., 97, 721 – 729.en_US
dc.identifier.citedreferencePenny, K. I. and Jolliffe, I. T. ( 1999 ) Multivariate outlier detection applied to multiply imputed laboratory data. Statist. Med., 18, 1879 – 1895.en_US
dc.identifier.citedreferenceRousseeuw, P. J. and van Zomeren, B. C. ( 1990 ) Unmasking multivariate outliers and leverage points (with comments). J. Am. Statist. Ass., 85, 633 – 651.en_US
dc.identifier.citedreferenceRubin, D. B. ( 1987 ) Multiple Imputation for Nonresponse in Surveys. New York: Wiley.en_US
dc.identifier.citedreferenceSAS Institute ( 2001 ) SAS Version 8.2. Cary: SAS Institute.en_US
dc.identifier.citedreferenceSchafer, J. L. ( 1997 ) Analysis of Incomplete Multivariate Data. London: Chapman and Hall.en_US
dc.identifier.citedreferenceSchwarz, G. ( 1978 ) Estimating the dimension of a model. Ann. Statist., 6, 461 – 464.en_US
dc.identifier.citedreferenceStephens, M. ( 2000 ) Dealing with label switching in mixture models. J. R. Statist. Soc. B, 62, 795 – 809.en_US
dc.identifier.citedreferenceStettler, N., Elliott, M. R., Kallan, M. Auerbach, S. B. and Kumanyika, S. K. ( 2005 ) High prevalence of pediatric overweight in medically underserved areas. Pediatrics, 116, 381 – 388.en_US
dc.identifier.citedreferenceTeicher, H. ( 1963 ) Identifyability of finite mixtures. Ann. Math. Statist., 34, 1265 – 1269.en_US
dc.identifier.citedreferenceWeiss, R., Dziura, J., Burgert, T. S., Tamborlane, W. V., Taksali, S. E., Yeckel, C. W., Allen, K., Lopes, M., Savoye, M., Morrison, J., Sherwin, R. S. and Caprio, S. ( 2004 ) Obesity and the metabolic syndrome in children and adolescents. New Engl. J. Med., 350, 2362 – 2374.en_US
dc.identifier.citedreferenceWoodruff, R. S. ( 1971 ) A simple method for approximating the variance of a complicated estimate. J. Am. Statist. Ass., 66, 411 – 414.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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