Accounting for non‐ignorable sampling and non‐response in statistical matching
Accounting for non‐ignorable sampling and non‐response in statistical matching
Data for statistical analysis is often available from different samples, with each sample containing measurements on only some of the variables of interest. Statistical matching attempts to generate a fused database containing matched measurements on all the target variables. In this article, we consider the use of statistical matching when the samples are drawn by informative sampling designs and are subject to not missing at random non-response. The problem with ignoring the sampling process and non-response is that the distribution of the data observed for the responding units can be very different from the distribution holding for the population data, which may distort the inference process and result in a matched database that misrepresents the joint distribution in the population. Our proposed methodology employs the empirical likelihood approach and is shown to perform well in a simulation experiment and when applied to real sample data.
IPF algorithm, NMAR non-response, empirical likelihood, fusion, matching uncertainty, sample and respondents distributions
Marella, Daniela
389be9c8-ff35-4ce5-91f6-3e29ccc53baa
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Marella, Daniela
389be9c8-ff35-4ce5-91f6-3e29ccc53baa
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Marella, Daniela and Pfeffermann, Danny
(2022)
Accounting for non‐ignorable sampling and non‐response in statistical matching.
International Statistical Review.
(doi:10.1111/insr.12524).
Abstract
Data for statistical analysis is often available from different samples, with each sample containing measurements on only some of the variables of interest. Statistical matching attempts to generate a fused database containing matched measurements on all the target variables. In this article, we consider the use of statistical matching when the samples are drawn by informative sampling designs and are subject to not missing at random non-response. The problem with ignoring the sampling process and non-response is that the distribution of the data observed for the responding units can be very different from the distribution holding for the population data, which may distort the inference process and result in a matched database that misrepresents the joint distribution in the population. Our proposed methodology employs the empirical likelihood approach and is shown to perform well in a simulation experiment and when applied to real sample data.
Text
Statistical matching-Informative sampling and nonresponse
- Accepted Manuscript
Text
Int Statistical Rev - 2022 - Marella - Accounting for Non‐ignorable Sampling and Non‐response in Statistical Matching
- Version of Record
More information
Accepted/In Press date: 26 September 2022
e-pub ahead of print date: 19 October 2022
Keywords:
IPF algorithm, NMAR non-response, empirical likelihood, fusion, matching uncertainty, sample and respondents distributions
Identifiers
Local EPrints ID: 472844
URI: http://eprints.soton.ac.uk/id/eprint/472844
ISSN: 0306-7734
PURE UUID: 7b13f52d-9070-41dd-a4dd-0b6783305ceb
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Date deposited: 20 Dec 2022 17:33
Last modified: 16 Mar 2024 23:19
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Author:
Daniela Marella
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