Outlier Robust Imputation of Survey Data via Reverse
Calibration
Outlier Robust Imputation of Survey Data via Reverse
Calibration
Outlier robust methods of survey estimation, e.g. trimming, winsorization, are well known (Chambers and Kokic, 1993). However, such methods do not address the important practical problem of creating an “outlier free” data set for general and public use. In particular, what is required in this situation is a data set from which the outlier robust survey estimate can be recovered by the application of standard methods of survey estimation. In this paper we describe an imputation procedure for outlying survey values, called reverse calibration, that achieves this aim. This method can also be used to correct gross errors in survey data, as well as to impute missing values. The paper concludes with an evaluation of the method based on a realistic survey data set.
Southampton Statistical Sciences Research Institute, University of Southampton
Ren, R.
bcd4ccbb-8e5e-4acc-9bca-918115ce0d28
Chambers, R.
a9a457b3-2dc5-4ff0-9ed3-dbb3a6901ed7
2003
Ren, R.
bcd4ccbb-8e5e-4acc-9bca-918115ce0d28
Chambers, R.
a9a457b3-2dc5-4ff0-9ed3-dbb3a6901ed7
Ren, R. and Chambers, R.
(2003)
Outlier Robust Imputation of Survey Data via Reverse
Calibration
(S3RI Methodology Working Papers, M03/19)
Southampton, UK.
Southampton Statistical Sciences Research Institute, University of Southampton
21pp.
Record type:
Monograph
(Project Report)
Abstract
Outlier robust methods of survey estimation, e.g. trimming, winsorization, are well known (Chambers and Kokic, 1993). However, such methods do not address the important practical problem of creating an “outlier free” data set for general and public use. In particular, what is required in this situation is a data set from which the outlier robust survey estimate can be recovered by the application of standard methods of survey estimation. In this paper we describe an imputation procedure for outlying survey values, called reverse calibration, that achieves this aim. This method can also be used to correct gross errors in survey data, as well as to impute missing values. The paper concludes with an evaluation of the method based on a realistic survey data set.
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Published date: 2003
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Local EPrints ID: 8169
URI: http://eprints.soton.ac.uk/id/eprint/8169
PURE UUID: 1ca93153-a1b4-441c-b292-c5c4612c165b
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Date deposited: 11 Jul 2004
Last modified: 15 Mar 2024 04:51
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Contributors
Author:
R. Ren
Author:
R. Chambers
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