Nonparametric Markov chain bootstrap for multiple imputation
Nonparametric Markov chain bootstrap for multiple imputation
Multiple imputation is a statistical method for analyzing data with missing values. Nonparametric Markov chain bootstrap methods can be used to generate multiple imputations of both scalar and multivariate outcome variables, under the assumption that the data are missing completely at random, and nonparametric inference can be obtained using multiple implementation bootstrap. The nonparametric approach is useful when parametric settings are inappropriate or difficult. An extension of the Markov chain bootstrap method is discussed under a more complex nonresponse assumption.
343-353
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
March 2004
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Zhang, Li-Chun
(2004)
Nonparametric Markov chain bootstrap for multiple imputation.
Computational Statistics & Data Analysis, 45 (2), .
(doi:10.1016/S0167-9473(02)00300-6).
Abstract
Multiple imputation is a statistical method for analyzing data with missing values. Nonparametric Markov chain bootstrap methods can be used to generate multiple imputations of both scalar and multivariate outcome variables, under the assumption that the data are missing completely at random, and nonparametric inference can be obtained using multiple implementation bootstrap. The nonparametric approach is useful when parametric settings are inappropriate or difficult. An extension of the Markov chain bootstrap method is discussed under a more complex nonresponse assumption.
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Published date: March 2004
Organisations:
Social Statistics & Demography
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Local EPrints ID: 356399
URI: http://eprints.soton.ac.uk/id/eprint/356399
ISSN: 0167-9473
PURE UUID: bc86efc7-cbd6-45f3-a5aa-ca5f934e5e45
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Date deposited: 18 Nov 2013 14:06
Last modified: 15 Mar 2024 03:45
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