Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

9-16-2009

Journal or Book Title

Biometrika

Volume

96

Issue

4

First Page

917

Last Page

932

DOI

10.1093/biomet/asp041

Abstract

Variance estimation after imputation is an important practical problem in survey sampling. When deterministic imputation or stochastic imputation is used, we show that the variance of the imputed estimator can be consistently estimated by a unifying linearize and reverse approach. We provide some applications of the approach to regression imputation, fractional categorical imputation, multiple imputation and composite imputation. Results from a simulation study, under a factorial structure for the sampling, response and imputation mechanisms, show that the proposed linearization variance estimator performs well in terms of relative bias, assuming a missing at random response mechanism.

Comments

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Biometrika following peer review. The version of record (Kim, Jae Kwang, and J. N. K. Rao. "A unified approach to linearization variance estimation from survey data after imputation for item nonresponse." Biometrika 96, no. 4 (2009): 917-932) is online at doi:10.1093/biomet/asp041. Posted with permission.

Copyright Owner

Biometrika Trust

Language

en

File Format

application/pdf

Published Version

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