Show simple item record

Model-based estimates of the finite population mean for two-stage cluster samples with unit non-response

dc.contributor.authorYuan, Yingen_US
dc.contributor.authorLittle, Roderick J. A.en_US
dc.date.accessioned2010-06-01T21:52:52Z
dc.date.available2010-06-01T21:52:52Z
dc.date.issued2007-01en_US
dc.identifier.citationYuan, Ying; Little, Roderick J. A. (2007). "Model-based estimates of the finite population mean for two-stage cluster samples with unit non-response." Journal of the Royal Statistical Society: Series C (Applied Statistics) 56(1): 79-97. <http://hdl.handle.net/2027.42/74917>en_US
dc.identifier.issn0035-9254en_US
dc.identifier.issn1467-9876en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/74917
dc.format.extent643944 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.otherCluster Samplingen_US
dc.subject.otherNon-ignorable Non-responseen_US
dc.subject.otherRandom-effects Modelen_US
dc.subject.otherUnit Non-responseen_US
dc.titleModel-based estimates of the finite population mean for two-stage cluster samples with unit non-responseen_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.affiliationotherUniversity of Texas M. D. Anderson Cancer Center, Houston, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/74917/1/j.1467-9876.2007.00566.x.pdf
dc.identifier.doi10.1111/j.1467-9876.2007.00566.xen_US
dc.identifier.sourceJournal of the Royal Statistical Society: Series C (Applied Statistics)en_US
dc.identifier.citedreferenceAlbert, J. and Chib, S. ( 1993 ) Bayesian analysis of binary and polychotomous response data. J. Am. Statist. Ass., 88, 669 – 679.en_US
dc.identifier.citedreferenceAmemiya, T. ( 1984 ) Tobit models: a survey. J. Econometr., 24, 3 – 61.en_US
dc.identifier.citedreferenceDe Heer, W. ( 1999 ) International response trends: results of an international survey. J. Off. Statist., 15, 129 – 142.en_US
dc.identifier.citedreferenceGelfand, A. E., Hills, S. E., Racine-Poon, A. and Smith, A. F. M. ( 1990 ) Illustration of Bayesian inference in normal data models using Gibbs sampling. J. Am. Statist. Ass., 85, 972 – 985.en_US
dc.identifier.citedreferenceGelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. ( 2004 ) Bayesian Data Analysis, 2nd edn. New York: Chapman and Hall.en_US
dc.identifier.citedreferenceGelman, A. and Rubin, D. B. ( 1992 ) Inference from iterative simulation using multiple sequences. Statist. Sci., 7, 457 – 511.en_US
dc.identifier.citedreferenceGroves, R. M. and Couper, M. P. ( 1998 ) Nonresponse in Household Interview Surveys. New York: Wiley.en_US
dc.identifier.citedreferenceHeckman, J. I. ( 1976 ) The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Ann. Econ. Socl Measmnt, 5, 475 – 492.en_US
dc.identifier.citedreferenceHorvitz, D. G. and Thompson, D. J. ( 1952 ) A generalization of sampling without replacement from a finite population. J. Am. Statist. Ass., 47, 663 – 685.en_US
dc.identifier.citedreferenceJohnson, V. E. ( 1996 ) Studying convergence of Markov Chain Monte Carlo algorithms using coupled sampled paths. J. Am. Statist. Ass., 91, 154 – 166.en_US
dc.identifier.citedreferenceKish, L. ( 1965 ) Survey Sampling. New York: Wiley.en_US
dc.identifier.citedreferenceKrosnick, J. A. ( 1991 ) Response strategies for coping with the cognitive demands of attitude measures in surveys. Appl. Cogn. Psychol., 5, 213 – 236.en_US
dc.identifier.citedreferenceLittle, R. J. A. ( 2003 ) The Bayesian approach to sample survey inference. In Analysis of Survey Data ( eds R. L. Chambers and C. J. Skinner ), pp. 49 – 57. New York: Wiley.en_US
dc.identifier.citedreferenceLittle, R. J. A. ( 2004 ) To model or not to model?: competing modes of inference for finite population sampling. J. Am. Statist. Ass., 99, 546 – 556.en_US
dc.identifier.citedreferenceLittle, R. J. A. and An, H. ( 2004 ) Robust likelihood-based analysis of multivariate data with missing values. Statist. Sin., 14, 949 – 968.en_US
dc.identifier.citedreferenceLittle, R. J. A. and Rubin, D. B. ( 2002 ) Statistical Analysis with Missing Data, 2nd edn. New York: Wiley.en_US
dc.identifier.citedreferenceLittle, R. J. A. and Vartivarian, S. ( 2002 ) On weighting the rates in nonresponse weights. Statist. Med., 22, 1589 – 1599.en_US
dc.identifier.citedreferenceRubin, D. B. ( 1976 ) Inference and missing data (with discussion). Biometrika, 63, 581 – 592.en_US
dc.identifier.citedreferenceScott, A. and Smith, T. M. F. ( 1969 ) Estimation in multi-stage surveys. J. Am. Statist. Ass., 64, 830 – 840.en_US
dc.identifier.citedreferenceSverchkov, M. and Pfeffermann, D. ( 2004 ) Prediction of finite population totals based on the sample distribution. Surv. Methodol., 30, 79 – 92.en_US
dc.identifier.citedreferenceTanner, M. A. and Wong, W. H. ( 1987 ) The calculation of posterior distributions by data augmentation. J. Am. Statist. Ass., 82, 528 – 550.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe its collections in a way that respects the people and communities who create, use, and are represented in them. We encourage you to Contact Us anonymously if you encounter harmful or problematic language in catalog records or finding aids. More information about our policies and practices is available at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.