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IMPROVED MODEL-ASSISTED ESTIMATION VIA PROBABILITY THRESHOLDING

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Title: IMPROVED MODEL-ASSISTED ESTIMATION VIA PROBABILITY THRESHOLDING
Authors: Zong, Xianpeng, Tso, Geoffrey K. F., Zou, Gouhua
Source: Zong, X, Tso, G K F & Zou, G 2026, 'IMPROVED MODEL-ASSISTED ESTIMATION VIA PROBABILITY THRESHOLDING', Statistica Sinica, vol. 36, no. 1, pp. 53-78. https://doi.org/10.5705/ss.202022.0295
Publication Year: 2026
Subject Terms: Horvitz-Thompson estimator, Model-assisted estimation, Probability thresholding, Superpopulation model, Survey sampling, COPYRIGHT TERMS OF DEPOSITED FINAL PUBLISHED VERSION FILE: Statistica Sinica © 2026 Institute of Statistical Science, Academia Sinica. Use of this article is permitted solely for educational and research purposes. Zong, X., Tso, G. K. F., & Zou, G. (2026). IMPROVED MODEL-ASSISTED ESTIMATION VIA PROBABILITY THRESHOLDING. Statistica Sinica, 36(1), 53-78. https://doi.org/10.5705/ss.202022.0295
Description: In survey sampling, model-assisted approach is often used to improve the precision of survey estimators when auxiliary information is available. Generally, the model-assisted estimators are nonlinear functions of some classical Horvitz-Thompson estimators constructed via inverse probability weighting, which are seriously a ected by the heterogeneous inclusion probabilities. In this paper, we improve the classical model-assisted estimation via probability thresholding, and propose the improved linear and nonparametric model-assisted estimators for nite populations. The proposed estimators are shown to be asymptotically design unbiased and design consistent. The corresponding design mean squared errors and their estimators are also derived. We theoretically prove that the new model-assisted estimators are asymptotically not worse than the commonly used model-assisted estimators. Two simulation examples and an empirical application indicate good nite sample performance of the proposed estimators.
Document Type: article in journal/newspaper
Language: English
ISSN: 1017-0405
1996-8507
Relation: info:eu-repo/semantics/altIdentifier/hdl/https://hdl.handle.net/2031/249bd303-8cbe-4a50-af6d-4537643da6d7; info:eu-repo/semantics/altIdentifier/pissn/1017-0405; info:eu-repo/semantics/altIdentifier/eissn/1996-8507
DOI: 10.5705/ss.202022.0295
Availability: https://scholars.cityu.edu.hk/en/publications/249bd303-8cbe-4a50-af6d-4537643da6d7
https://doi.org/10.5705/ss.202022.0295
https://hdl.handle.net/2031/249bd303-8cbe-4a50-af6d-4537643da6d7
Rights: info:eu-repo/semantics/embargoedAccess
Accession Number: edsbas.684647C4
Database: BASE
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  – Url: https://scholars.cityu.edu.hk/en/publications/249bd303-8cbe-4a50-af6d-4537643da6d7#
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  Data: IMPROVED MODEL-ASSISTED ESTIMATION VIA PROBABILITY THRESHOLDING
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  Data: <searchLink fieldCode="AR" term="%22Zong%2C+Xianpeng%22">Zong, Xianpeng</searchLink><br /><searchLink fieldCode="AR" term="%22Tso%2C+Geoffrey+K%2E+F%2E%22">Tso, Geoffrey K. F.</searchLink><br /><searchLink fieldCode="AR" term="%22Zou%2C+Gouhua%22">Zou, Gouhua</searchLink>
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  Data: Zong, X, Tso, G K F & Zou, G 2026, 'IMPROVED MODEL-ASSISTED ESTIMATION VIA PROBABILITY THRESHOLDING', Statistica Sinica, vol. 36, no. 1, pp. 53-78. https://doi.org/10.5705/ss.202022.0295
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  Data: 2026
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  Data: <searchLink fieldCode="DE" term="%22Horvitz-Thompson+estimator%22">Horvitz-Thompson estimator</searchLink><br /><searchLink fieldCode="DE" term="%22Model-assisted+estimation%22">Model-assisted estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+thresholding%22">Probability thresholding</searchLink><br /><searchLink fieldCode="DE" term="%22Superpopulation+model%22">Superpopulation model</searchLink><br /><searchLink fieldCode="DE" term="%22Survey+sampling%22">Survey sampling</searchLink><br /><searchLink fieldCode="DE" term="%22COPYRIGHT+TERMS+OF+DEPOSITED+FINAL+PUBLISHED+VERSION+FILE%3A+Statistica+Sinica+©+2026+Institute+of+Statistical+Science%22">COPYRIGHT TERMS OF DEPOSITED FINAL PUBLISHED VERSION FILE: Statistica Sinica © 2026 Institute of Statistical Science</searchLink><br /><searchLink fieldCode="DE" term="%22Academia+Sinica%2E+Use+of+this+article+is+permitted+solely+for+educational+and+research+purposes%2E+Zong%22">Academia Sinica. Use of this article is permitted solely for educational and research purposes. Zong</searchLink><br /><searchLink fieldCode="DE" term="%22X%2E%22">X.</searchLink><br /><searchLink fieldCode="DE" term="%22Tso%22">Tso</searchLink><br /><searchLink fieldCode="DE" term="%22G%2E+K%2E+F%2E%22">G. K. F.</searchLink><br /><searchLink fieldCode="DE" term="%22%26+Zou%22">& Zou</searchLink><br /><searchLink fieldCode="DE" term="%22G%2E+%282026%29%2E+IMPROVED+MODEL-ASSISTED+ESTIMATION+VIA+PROBABILITY+THRESHOLDING%2E+Statistica+Sinica%22">G. (2026). IMPROVED MODEL-ASSISTED ESTIMATION VIA PROBABILITY THRESHOLDING. Statistica Sinica</searchLink><br /><searchLink fieldCode="DE" term="%2236%281%29%22">36(1)</searchLink><br /><searchLink fieldCode="DE" term="%2253-78%2E+https%3A%2F%2Fdoi%2Eorg%2F10%2E5705%2Fss%2E202022%2E0295%22">53-78. https://doi.org/10.5705/ss.202022.0295</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: In survey sampling, model-assisted approach is often used to improve the precision of survey estimators when auxiliary information is available. Generally, the model-assisted estimators are nonlinear functions of some classical Horvitz-Thompson estimators constructed via inverse probability weighting, which are seriously a ected by the heterogeneous inclusion probabilities. In this paper, we improve the classical model-assisted estimation via probability thresholding, and propose the improved linear and nonparametric model-assisted estimators for nite populations. The proposed estimators are shown to be asymptotically design unbiased and design consistent. The corresponding design mean squared errors and their estimators are also derived. We theoretically prove that the new model-assisted estimators are asymptotically not worse than the commonly used model-assisted estimators. Two simulation examples and an empirical application indicate good nite sample performance of the proposed estimators.
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  Data: 10.5705/ss.202022.0295
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  Data: https://scholars.cityu.edu.hk/en/publications/249bd303-8cbe-4a50-af6d-4537643da6d7<br />https://doi.org/10.5705/ss.202022.0295<br />https://hdl.handle.net/2031/249bd303-8cbe-4a50-af6d-4537643da6d7
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      – Text: English
    Subjects:
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        Type: general
      – SubjectFull: Model-assisted estimation
        Type: general
      – SubjectFull: Probability thresholding
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      – SubjectFull: Superpopulation model
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      – SubjectFull: Survey sampling
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      – SubjectFull: COPYRIGHT TERMS OF DEPOSITED FINAL PUBLISHED VERSION FILE: Statistica Sinica © 2026 Institute of Statistical Science
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      – SubjectFull: G. K. F.
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      – SubjectFull: G. (2026). IMPROVED MODEL-ASSISTED ESTIMATION VIA PROBABILITY THRESHOLDING. Statistica Sinica
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      – SubjectFull: 36(1)
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