期刊论文

Propensity Score Model Inference Method of Non-probability Samples Based on Model-X Knockoffs

中文

统计与决策

1002-6487

2023

39

04

10-15

10.13546/j.cnki.tjyjc.2023.04.002

国家社会科学基金一般项目（18BTJ022）；

本校为其他机构

大数据下的样本大多为非概率样本,其入样概率未知，同时可能面临着协变量较多甚至是高维的情况，那么如何对这种情况下的非概率样本进行推断值得探索。针对该问题，文章考虑到Model-X Knockoffs的降维特点，提出采用Model-X Knockoffs筛选出重要变量，建立Logistic倾向得分模型来估计非概率样本的入样概率或倾向得分，对总体进行推断，从而提高估计的精度，同时可控制变量选择的错误发现率与功效。模拟与实证研究结果表明：基于Model-X Knockoffs的Logistic倾向得分模型的总体均值估计相比一般的Logistic倾向得分模型和广义线性回归模型的总体均值估计，偏差更小、效率更高、估计效果更好，并且...

The samples under big data are mostly non-probabilistic samples with unknown inclusion probabilities, and may also face the situation of more covariables or even high dimensions. To solve this problem, this paper considers the dimensionality reduction characteristics of...MORE The samples under big data are mostly non-probabilistic samples with unknown inclusion probabilities, and may also face the situation of more covariables or even high dimensions. To solve this problem, this paper considers the dimensionality reduction characteristics of Model-X Knockoffs and proposes the method of using ...