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Nonparametric Markov chain bootstrap for multiple imputation

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.
0167-9473
343-353
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
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), 343-353. (doi:10.1016/S0167-9473(02)00300-6).

Record type: Article

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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More information

Published date: March 2004
Organisations: Social Statistics & Demography

Identifiers

Local EPrints ID: 356399
URI: http://eprints.soton.ac.uk/id/eprint/356399
ISSN: 0167-9473
PURE UUID: bc86efc7-cbd6-45f3-a5aa-ca5f934e5e45
ORCID for Li-Chun Zhang: ORCID iD orcid.org/0000-0002-3944-9484

Catalogue record

Date deposited: 18 Nov 2013 14:06
Last modified: 15 Mar 2024 03:45

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