Fully nonparametric inverse probability weighting estimation with nonignorable missing data and its extension to missing quantile regression
In practical data analysis, the not-missing-at-random (NMAR) mechanism is typically more aligned with the natural causes of missing data. The NMAR mechanism is complicated and adaptable, surpassing the capabilities of classical methods in addressing this missing data challenge. A comprehensive analy...
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| Published in | Computational statistics & data analysis Vol. 206; p. 108127 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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01.06.2025
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| Abstract | In practical data analysis, the not-missing-at-random (NMAR) mechanism is typically more aligned with the natural causes of missing data. The NMAR mechanism is complicated and adaptable, surpassing the capabilities of classical methods in addressing this missing data challenge. A comprehensive analysis framework for the NMAR problem is established, and a novel inverse probability weighting method based on the fully nonparametric exponential tilting model and sieve minimum distance is constructed. Additionally, given the broad field of applications for the quantile regression model, fully nonparametric inverse probability weighting and augmented inverse probability weighting for estimating quantile regression under NMAR are introduced. Simulation studies demonstrate that the proposed methods are better suited for various flexible propensity score functions. In practical applications, our methods are applied to the AIDS Clinical Trials Group Study 175 data to examine the effectiveness of treatments on HIV-infected subjects. |
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| AbstractList | In practical data analysis, the not-missing-at-random (NMAR) mechanism is typically more aligned with the natural causes of missing data. The NMAR mechanism is complicated and adaptable, surpassing the capabilities of classical methods in addressing this missing data challenge. A comprehensive analysis framework for the NMAR problem is established, and a novel inverse probability weighting method based on the fully nonparametric exponential tilting model and sieve minimum distance is constructed. Additionally, given the broad field of applications for the quantile regression model, fully nonparametric inverse probability weighting and augmented inverse probability weighting for estimating quantile regression under NMAR are introduced. Simulation studies demonstrate that the proposed methods are better suited for various flexible propensity score functions. In practical applications, our methods are applied to the AIDS Clinical Trials Group Study 175 data to examine the effectiveness of treatments on HIV-infected subjects. |
| ArticleNumber | 108127 |
| Author | Tao, Li Tian, Maozai Tang, Man-lai Pan, Jianxin Yu, Keming Härdle, Wolfgang Karl Tai, Lingnan |
| Author_xml | – sequence: 1 givenname: Lingnan surname: Tai fullname: Tai, Lingnan organization: School of Economics and Management, The Open University of China, Beijing, China – sequence: 2 givenname: Li surname: Tao fullname: Tao, Li organization: School of Information, Beijing Wuzi University, Beijing, China – sequence: 3 givenname: Jianxin surname: Pan fullname: Pan, Jianxin organization: School of Mathematics, University of Manchester, Manchester, United Kingdom – sequence: 4 givenname: Man-lai surname: Tang fullname: Tang, Man-lai organization: School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom – sequence: 5 givenname: Keming surname: Yu fullname: Yu, Keming organization: Mathematical Sciences, Brunel University, Uxbridge, United Kingdom – sequence: 6 givenname: Wolfgang Karl surname: Härdle fullname: Härdle, Wolfgang Karl organization: Business and Economics, Humboldt-Universität zu Berlin, Berlin, Germany – sequence: 7 givenname: Maozai orcidid: 0000-0002-0515-4477 surname: Tian fullname: Tian, Maozai email: mztian@ruc.edu.cn organization: Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, 100872, China |
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| Keywords | Inverse probability weighting Quantile regression Nonparametric propensity score Sieve minimum distance Not missing at random |
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| SubjectTerms | data analysis HIV infections Inverse probability weighting Nonparametric propensity score Not missing at random probability Quantile regression regression analysis Sieve minimum distance |
| Title | Fully nonparametric inverse probability weighting estimation with nonignorable missing data and its extension to missing quantile regression |
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