Nonlinear Superpopulation Model Inference for Non‐Probability Samples With Nonignorable Missingness.
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| Title: | Nonlinear Superpopulation Model Inference for Non‐Probability Samples With Nonignorable Missingness. |
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| Authors: | Liu, Zhan1,2 (AUTHOR), Xu, Jiajing1 (AUTHOR), Li, Ruohan1 (AUTHOR), Pan, Yingli1,2 (AUTHOR) panyingli220@163.com |
| Source: | Statistical Analysis & Data Mining. Oct2025, Vol. 18 Issue 5, p1-13. 13p. |
| Subjects: | Nonprobability sampling, Missing data (Statistics), Inferential statistics, Simulation methods & models, Selection bias (Statistics), Nonlinear estimation, Asymptotic analysis |
| Abstract: | In the era of big data and increasing survey costs, non‐probability samples become increasingly popular in sampling surveys but may be subject to selection biases with unknown inclusion probabilities. In addition to selection bias, non‐probability samples may also suffer from missingness in practice, even nonignorable nonresponse with unknown response probabilities. Superpopulation model methods have been discussed for reducing selection biases from non‐probability samples, but not for dealing with nonignorable missingness for non‐probability samples yet. In this paper, we propose a nonlinear superpopulation model inference approach for non‐probability samples with nonignorable missingness. We develop rigorous procedures for estimating the response probabilities for units in the non‐probability sample and the nonlinear superpopulation model parameters, and construct the finite population mean estimator based on the nonlinear superpopulation model, response probabilities, and inclusion probabilities. Asymptotic properties of the proposed estimators and variance estimation are also discussed under the proposed framework. In a simulation study, we compare the proposed method with some existing methods. The real data analysis results using the National Health Interview Survey data illustrate the good performance of the proposed method. [ABSTRACT FROM AUTHOR] |
| Copyright of Statistical Analysis & Data Mining is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 188873649 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Nonlinear Superpopulation Model Inference for Non‐Probability Samples With Nonignorable Missingness. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Zhan%22">Liu, Zhan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Jiajing%22">Xu, Jiajing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Ruohan%22">Li, Ruohan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pan%2C+Yingli%22">Pan, Yingli</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> panyingli220@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Statistical+Analysis+%26+Data+Mining%22">Statistical Analysis & Data Mining</searchLink>. Oct2025, Vol. 18 Issue 5, p1-13. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Nonprobability+sampling%22">Nonprobability sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Missing+data+%28Statistics%29%22">Missing data (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Inferential+statistics%22">Inferential statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Selection+bias+%28Statistics%29%22">Selection bias (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+estimation%22">Nonlinear estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Asymptotic+analysis%22">Asymptotic analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the era of big data and increasing survey costs, non‐probability samples become increasingly popular in sampling surveys but may be subject to selection biases with unknown inclusion probabilities. In addition to selection bias, non‐probability samples may also suffer from missingness in practice, even nonignorable nonresponse with unknown response probabilities. Superpopulation model methods have been discussed for reducing selection biases from non‐probability samples, but not for dealing with nonignorable missingness for non‐probability samples yet. In this paper, we propose a nonlinear superpopulation model inference approach for non‐probability samples with nonignorable missingness. We develop rigorous procedures for estimating the response probabilities for units in the non‐probability sample and the nonlinear superpopulation model parameters, and construct the finite population mean estimator based on the nonlinear superpopulation model, response probabilities, and inclusion probabilities. Asymptotic properties of the proposed estimators and variance estimation are also discussed under the proposed framework. In a simulation study, we compare the proposed method with some existing methods. The real data analysis results using the National Health Interview Survey data illustrate the good performance of the proposed method. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Statistical Analysis & Data Mining is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/sam.70040 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Nonprobability sampling Type: general – SubjectFull: Missing data (Statistics) Type: general – SubjectFull: Inferential statistics Type: general – SubjectFull: Simulation methods & models Type: general – SubjectFull: Selection bias (Statistics) Type: general – SubjectFull: Nonlinear estimation Type: general – SubjectFull: Asymptotic analysis Type: general Titles: – TitleFull: Nonlinear Superpopulation Model Inference for Non‐Probability Samples With Nonignorable Missingness. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Zhan – PersonEntity: Name: NameFull: Xu, Jiajing – PersonEntity: Name: NameFull: Li, Ruohan – PersonEntity: Name: NameFull: Pan, Yingli IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19321864 Numbering: – Type: volume Value: 18 – Type: issue Value: 5 Titles: – TitleFull: Statistical Analysis & Data Mining Type: main |
| ResultId | 1 |
