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因果推断中匹配与加权调整手册AI 解读

For observational studies, causal inference often relies on adjustment methods for the adjustment of confounding effects. The research of adjustment methods has a long history, and, in recent years...

离线强化学习中的投影状态—动作平衡权重AI 解读

Off-policy evaluation is considered a fundamental and challenging problem in reinforcement learning (RL). This paper focuses on value estimation of a target policy based on pre-collected data generated from a possibly different policy, under the framework of infinite-horizon Markov decision processes. Motivated by the recently developed marginal importance sampling method in RL and the covariate balancing idea in causal inference, we propose a novel estimator with…

基于小波的函数型数据独立性检验及脑磁图连接应用AI 解读

Measuring and testing the dependency between multiple random functions is often an important task in functional data analysis. In the literature, a model-based method relies on a model which is subject to the risk of model misspecification, while a model-free method only provides a correlation measure which is inadequate to test independence. In this paper, we adopt the Hilbert–Schmidt Independence Criterion (HSIC) to measure the dependency between two random functions. We…

多维函数型数据的低秩协方差函数估计AI 解读

Jiayi Wanga, Raymond K. W. Wonga* & Xiaoke Zhangb a Department of Statistics, Texas A&M University, College Station, TX; b Department of Statistics, George Washington University, Washington, DC Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA. CONTACT Raymond K. W. Wong raywong@tamu.edu Department of Statistics, Texas A&M University , College Station , TX 77843.AbstractMultidimensional function data arise from many fields…

多元函数型数据的部分线性函数型可加模型AI 解读

Raymond K. W. Wonga, Yehua Lib* http://orcid.org/0000-0002-5945-378X & Zhengyuan Zhuca Department of Statistics, Texas A&M University, College Station, TXb Department of Statistics, University of California, Riverside, CAc Department of Statistics & Statistical Laboratory, Iowa State University, Ames, IACONTACT Yehua Li yehuali@ucr.edu Department of Statistics, University of California, Riverside, CA 92521Color versions of one or more of the figures in the article can be…

结合协变量信息的矩阵补全AI 解读

This article investigates the problem of matrix completion from the corrupted data, when the additional covariates are available. Despite being seldomly considered in the matrix completion literature, these covariates often provide valuable information for completing the unobserved entries of the high-dimensional target matrix A0. Given a covariate matrix X with its rows representing the row covariates of A0, we consider a column-space-decomposition model A0 = Xβ0 + B0,…

计算机模型校准的频率学派方法AI 解读

Summary The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes…