Imputation under informative sampling

Berg, Emily, Kim, J. K. and Skinner, Chris (2016) Imputation under informative sampling. Journal of Survey Statistics and Methodology, 4 (4). pp. 436-462. ISSN 2325-0984

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Abstract

Imputed values in surveys are often generated under the assumption that the sampling mechanism is non-informative (or ignorable) and the study variable is missing at random (MAR). When the sampling design is informative, the assumption of MAR in the population does not necessarily imply MAR in the sample. In this case, the classical method of imputation using a model fitted to the sample data does not in general lead to unbiased estimation. To overcome this problem, we consider alternative approaches to imputation assuming MAR in the population. We compare the alternative imputation procedures through simulation and an application to estimation of mean erosion using data from the Conservation Effects Assessment Project.

Item Type:	Article
Official URL:	http://www.oxfordjournals.org/our_journals/jssam
Additional Information:	© 2016 The Authors
Divisions:	Statistics
Subjects:	Q Science > QA Mathematics
Date Deposited:	29 Feb 2016 13:48
Last Modified:	12 Dec 2024 01:09
URI:	http://eprints.lse.ac.uk/id/eprint/65553

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