Turnover flash estimation by purposive sampling and debit card transactions
Turnover flash estimation by purposive sampling and debit card transactions
Improving timeliness is ever more urgent for official statistic, not least due to the potentials of various non-survey big data that in principle can be made more quickly available than traditional sample surveys. Taking Retail Turnover Index as the case-in-point, we develop new approaches of model learning aimed to achieve flash estimation of acceptable accuracy, as well as the associated methods of uncertainty assessment, when one does not have the target observations that would have been required for unbiased inference by established statistical theories. Applications to the Norwegian data will be used to demonstrate the efficacy of our proposals.
Augmented learning, quasi transfer learning, validation of model or learning, error prediction
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Haug, Jens Kristoffer
eb150eb1-d1fd-4710-8551-d474747d4060
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Haug, Jens Kristoffer
eb150eb1-d1fd-4710-8551-d474747d4060
Zhang, Li-Chun and Haug, Jens Kristoffer
(2024)
Turnover flash estimation by purposive sampling and debit card transactions.
Journal of Official Statistics.
(In Press)
Abstract
Improving timeliness is ever more urgent for official statistic, not least due to the potentials of various non-survey big data that in principle can be made more quickly available than traditional sample surveys. Taking Retail Turnover Index as the case-in-point, we develop new approaches of model learning aimed to achieve flash estimation of acceptable accuracy, as well as the associated methods of uncertainty assessment, when one does not have the target observations that would have been required for unbiased inference by established statistical theories. Applications to the Norwegian data will be used to demonstrate the efficacy of our proposals.
Text
JOS-2024-0014-final
- Accepted Manuscript
More information
Accepted/In Press date: 26 September 2024
Keywords:
Augmented learning, quasi transfer learning, validation of model or learning, error prediction
Identifiers
Local EPrints ID: 495537
URI: http://eprints.soton.ac.uk/id/eprint/495537
ISSN: 0282-423X
PURE UUID: 3ce417f8-f3b9-465a-8363-8ff26ae254ae
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Date deposited: 15 Nov 2024 17:55
Last modified: 16 Nov 2024 02:45
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Contributors
Author:
Jens Kristoffer Haug
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