Nonlinear Superpopulation Model Inference for Non‐Probability Samples With Nonignorable Missingness.
Saved in:
| Title: | Nonlinear Superpopulation Model Inference for Non‐Probability Samples With Nonignorable Missingness. |
|---|---|
| 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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
