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Shan、Ying 与 Zhao 对《作为线性回归的增广平衡权重》的讨论AI 解读

Jiawei Shan, Chao Ying, Jiwei Zhao; Shan, Ying and Zhao’s contribution to the Discussion of ‘Augmented balancing weights as linear regression' by Bruns-Smi

标签分布迁移下的双重灵活估计AI 解读

In studies ranging from clinical medicine to policy research, complete data are usually available from a population P, but the quantity of interest is often sought for a related but different population Q which only has partial data. We consider the setting when both outcome Y and covariate X are available from P but only X is available from Q, under the label shift assumption; that is, the conditional distribution of X given Y is the same in the two populations. To estimate…

高维半监督学习的最优与安全估计AI 解读

We consider the estimation problem in high-dimensional semi-supervised learning. Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation of the regression parameters of linear model in light of the fact that such linear models may be misspecified in data analysis. We first establish the minimax lower bound for parameter estimation in the semi-supervised setting, and show that this lower bound cannot be achieved by supervised…

高维阈值参数的显著性检验：个体化最小临床重要差异应用AI 解读

This work is motivated by learning the individualized minimal clinically important difference, a vital concept to assess clinical importance in various biomedical studies. We formulate the scientific question into a high-dimensional statistical problem where the parameter of interest lies in an individualized linear threshold. The goal is to develop a hypothesis testing procedure for the significance of a single element in this parameter as well as of a linear combination of…

无需估计非可忽略缺失机制的通用估计程序AI 解读

We consider the estimation problem in a regression setting where the outcome variable is subject to nonignorable missingness and identifiability is ensured by the shadow variable approach. We propose a versatile estimation procedure where modeling of missingness mechanism is completely bypassed. We show that our estimator is easy to implement and we derive the asymptotic theory of the proposed estimator. We also investigate some alternative estimators under different…

二元响应下高维个体化阈值的非正则与极小极大估计AI 解读

Given a large number of covariates Z, we consider the estimation of a high-dimensional parameter θ in an individualized linear threshold θTZ for a continuous variable X, which minimizes the disagreement between sign(X−θTZ) and a binary response Y. While the problem can be formulated into the M-estimation framework, minimizing the corresponding empirical risk function is computationally intractable due to discontinuity of the sign function. Moreover, estimating θ even in the…

赵继伟对《广义线性模型参数的弱假设推断》的讨论AI 解读

First I congratulate Vansteelandt and Dukes for a fascinating and stimulating paper, which I believe will have great impact on the practical data analysis as well as on the application of semiparametric techniques. In applications, any fitted model could be a misspecified model, for example Ê(A|L) converges to E*(A|L)⁠, where E* denotes the expectation that is evaluated under a working model. The similar notations are also used for Cov and Var throughout. Nonetheless, I…

不完整数据下分位数估计的一般框架AI 解读

Summary Quantile estimation has attracted significant research interest in recent years. However, there has been only a limited literature on quantile estimation in the presence of incomplete data. We propose a general framework to address this problem. Our framework combines the two widely adopted approaches for missing data analysis, the imputation approach and the inverse probability weighting approach, via the empirical likelihood method. The method proposed is capable…

不可忽略无应答下多元缺失数据分析的最优伪似然估计AI 解读

Tang et al. (2003) considered a regression model with missing response, where the missingness mechanism depends on the value of the response variable and hence is nonignorable. They proposed three pseudolikelihood estimators, based on different treatments of the probability distribution of the completely observed covariates. The first assumes the distribution of the covariate to be known, the second estimates this distribution parametrically, and the third estimates the…

不可忽略缺失数据下广义线性模型的半参数伪似然AI 解读

We consider identifiability and estimation in a generalized linear model in which the response variable and some covariates have missing values and the missing data mechanism is nonignorable and unspecified. We adopt a pseudo-likelihood approach that makes use of an instrumental variable to help identifying unknown parameters in the presence of nonignorable missing data. Explicit conditions for the identifiability of parameters are given. Some asymptotic properties of the…