Response Model Selection in Small Area Estimation Under not Missing at Random Nonresponse
Response Model Selection in Small Area Estimation Under not Missing at Random Nonresponse
Sverchkov and Pfeffermann[16] consider Small Area Estimation (SAE) under informative probability sampling of areas and within the sampled areas, and not missing at random (NMAR) nonresponse. To account for the nonresponse, the authors assume a given response model, which contains the outcome values as one of the covariates and estimate the corresponding response probabilities by application of the Missing Information Principle, which consists of defining the likelihood as if there was complete response and then integrating out the unobserved outcomes from the likelihood by employing the relationship between the distributions of the observed and the missing data. A key condition for the success of this approach is the ‘correct’ specification of the response model. In this article, we consider the likelihood ratio test and information criteria based on the appropriate likelihood and show how they can be used for the selection of the response model. We illustrate the approach by a small simulation study.
AIC, BIC information criteria, likelihood ratio test, population distribution, respondents’ model, sample distribution
173-183
Sverchkov, Michael
e55f2540-b8a5-4142-9645-347834040e09
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
November 2023
Sverchkov, Michael
e55f2540-b8a5-4142-9645-347834040e09
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Sverchkov, Michael and Pfeffermann, Danny
(2023)
Response Model Selection in Small Area Estimation Under not Missing at Random Nonresponse.
Calcutta Statistical Association Bulletin, 75 (2), .
(doi:10.1177/00080683231197291).
Abstract
Sverchkov and Pfeffermann[16] consider Small Area Estimation (SAE) under informative probability sampling of areas and within the sampled areas, and not missing at random (NMAR) nonresponse. To account for the nonresponse, the authors assume a given response model, which contains the outcome values as one of the covariates and estimate the corresponding response probabilities by application of the Missing Information Principle, which consists of defining the likelihood as if there was complete response and then integrating out the unobserved outcomes from the likelihood by employing the relationship between the distributions of the observed and the missing data. A key condition for the success of this approach is the ‘correct’ specification of the response model. In this article, we consider the likelihood ratio test and information criteria based on the appropriate likelihood and show how they can be used for the selection of the response model. We illustrate the approach by a small simulation study.
Text
CSA 2023 0009
- Accepted Manuscript
More information
Accepted/In Press date: 18 May 2023
e-pub ahead of print date: 29 October 2023
Published date: November 2023
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Publisher Copyright:
© 2023 Calcutta Statistical Association, Kolkata.
Keywords:
AIC, BIC information criteria, likelihood ratio test, population distribution, respondents’ model, sample distribution
Identifiers
Local EPrints ID: 483357
URI: http://eprints.soton.ac.uk/id/eprint/483357
ISSN: 0008-0683
PURE UUID: 9e85ebec-3abb-4221-ad40-1510db6c373f
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Date deposited: 30 Oct 2023 09:19
Last modified: 17 Mar 2024 07:45
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Author:
Michael Sverchkov
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