Adjusting misclassification using a second classifier with an external validation sample
Adjusting misclassification using a second classifier with an external validation sample
Administrative data may suffer from delays or mistakes in reporting. To adjust for the resulting measurement errors, it is often necessary to combine data from related sources, such as sample survey, administrative or ‘big’ data. However, the additional measure variable usually has a different definition and errors of its own, and the available joint data set may not have a completely known sampling distribution. We develop a modelling approach which capitalizes on one's knowledge and experience with the data source where they exist, and apply it to register- and survey-based Employed status. Comparisons are made to adjustments by hidden Markov models. Our approach is applicable to similar situations involving big data sources.
calibration probability, discriminant, matrix method, maximum likelihood estimation, misclassification
1882-1902
Schenkel, Jonas
4716d7a4-c5d8-45f6-8185-2169e51cea14
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
October 2022
Schenkel, Jonas
4716d7a4-c5d8-45f6-8185-2169e51cea14
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Schenkel, Jonas and Zhang, Li-Chun
(2022)
Adjusting misclassification using a second classifier with an external validation sample.
Journal of the Royal Statistical Society: Series A (Statistics in Society), 185 (4), .
(doi:10.1111/rssa.12845).
Abstract
Administrative data may suffer from delays or mistakes in reporting. To adjust for the resulting measurement errors, it is often necessary to combine data from related sources, such as sample survey, administrative or ‘big’ data. However, the additional measure variable usually has a different definition and errors of its own, and the available joint data set may not have a completely known sampling distribution. We develop a modelling approach which capitalizes on one's knowledge and experience with the data source where they exist, and apply it to register- and survey-based Employed status. Comparisons are made to adjustments by hidden Markov models. Our approach is applicable to similar situations involving big data sources.
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JRSSA-misclassification-Accepted
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Accepted/In Press date: 27 February 2022
Published date: October 2022
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Publisher Copyright:
© 2022 The Authors. Journal of the Royal Statistical Society: Series A (Statistics in Society) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society.
Keywords:
calibration probability, discriminant, matrix method, maximum likelihood estimation, misclassification
Identifiers
Local EPrints ID: 455752
URI: http://eprints.soton.ac.uk/id/eprint/455752
ISSN: 0964-1998
PURE UUID: 7ceff3c0-26b3-40a3-a000-be54463a5c46
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Date deposited: 01 Apr 2022 16:37
Last modified: 17 Mar 2024 07:10
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Author:
Jonas Schenkel
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