不可忽略缺失数据的全非参数逆概率加权估计及其在缺失分位数回归中的推广

IF 1.6 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computational Statistics & Data Analysis Pub Date : 2025-01-20 DOI:10.1016/j.csda.2025.108127

Lingnan Tai , Li Tao , Jianxin Pan , Man-lai Tang , Keming Yu , Wolfgang Karl Härdle , Maozai Tian

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引用次数: 0

摘要

在实际的数据分析中，非随机缺失（NMAR）机制通常更符合丢失数据的自然原因。NMAR机制复杂且适应性强，在解决这一缺失数据挑战方面超越了传统方法的能力。建立了NMAR问题的综合分析框架，构造了一种基于全非参数指数倾斜模型和筛最小距离的反概率加权方法。此外，考虑到分位数回归模型的广泛应用领域，介绍了NMAR下估计分位数回归的全非参数逆概率加权和增广逆概率加权。仿真研究表明，所提出的方法更适合于各种灵活的倾向分数函数。在实际应用中，我们的方法被应用于艾滋病临床试验组研究175的数据，以检查治疗对艾滋病毒感染者的有效性。

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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 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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来源期刊

Computational Statistics & Data Analysis 数学-计算机：跨学科应用

CiteScore

3.70

自引率

5.60%

发文量

167

审稿时长

60 days

期刊介绍： Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas: I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article. II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures. [...] III) Special Applications - [...] IV) Annals of Statistical Data Science [...]