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缺失数据问题中高效的增广逆概率加权估计AI 解读

When analyzing data with missing data, a commonly used method is the inverse probability weighting (IPW) method, which reweights estimating equations with propensity scores. The popularity of the IPW method is due to its simplicity. However, it is often being criticized for being inefficient because most of the information from the incomplete observations is not used. Alternatively, the regression method is known to be efficient but is nonrobust to the misspecification of…

缺失数据问题中的经验似然AI 解读

Missing data is a ubiquitous problem in medical and social sciences. It is well known that inferences based only on the complete data may not only lose efficiency, but may also lead to biased results if the data is not missing completely at random (MCAR). The inverse-probability weighting method proposed by Horvitz and Thompson (1952) is a popular alternative when the data is not MCAR. The Horvitz–Thompson method, however, is sensitive to the inverse weights and may suffer…

分布性及一般处理效应的半参数估计与推断AI 解读

Summary There is a large literature on methods of analysis for randomized trials with noncompliance which focuses on the effect of treatment on the average outcome. The paper considers evaluating the effect of treatment on the entire distribution and general functions of this effect. For distributional treatment effects, fully non-parametric and fully parametric approaches have been proposed. The fully non-parametric approach could be inefficient but the fully parametric…

协变量依赖的响应缺失下高效且双重稳健的插补AI 解读

In this article we study a well-known response missing-data problem. Missing data is an ubiquitous problem in medical and social science studies. Imputation is one of the most popular methods for dealing with missing data. The most commonly used imputation that makes use of covariates is regression imputation, in which the regression model can be parametric, semiparametric, or nonparametric. Parametric regression imputation is efficient but is not robust against…

基于经验似然的双重差分估计量AI 解读

Summary Recently there has been a surge in econometric and epidemiologic works focusing on estimating average treatment effects under various sets of assumptions. Estimation of average treatment effects in observational studies often requires adjustment for differences in pretreatment variables. Rosenbaum and Rubin have proposed the propensity score method for estimating the average treatment effect by adjusting pretreatment variables. In this paper, the empirical likelihood…

缺失响应问题中的经验似然推断及其观察性研究应用AI 解读

Summary The problem of missing response data is ubiquitous in medical and social science studies. In the case of responses that are missing at random (depending on some covariate information), analyses focused only on the complete data may lead to biased results. Various debias methods have been extensively studied in the literature, particularly the weighting method that was motivated by Horvitz and Thompson's estimators. To improve efficiency, Robins, Rotnitzky and Zhao…

边际似然、条件似然与经验似然的联系及应用AI 解读

Marginal likelihood and conditional likelihood are often used for eliminating nuisance parameters. For a parametric model, it is well known that the full likelihood can be decomposed into the product of a conditional likelihood and a marginal likelihood. This property is less transparent in a nonparametric or semiparametric likelihood setting. In this paper we show that this nice parametric likelihood property can be carried over to the empirical likelihood world. We discuss…

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