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作者：Huling, Jared D.; Greifer, Noah; Chen, Guanhua
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Harvard University; University of Wisconsin System; University of Wisconsin Madison; University of Minnesota System; University of Minnesota Twin Cities
摘要：Studying causal effects of continuous treatments is important for gaining a deeper understanding of many interventions, policies, or medications, yet researchers are often left with observational studies for doing so. In the observational setting, confounding is a barrier to the estimation of causal effects. Weighting approaches seek to control for confounding by reweighting samples so that confounders are comparable across different treatment values. Yet, for continuous treatments, weighting ...
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作者：Kim, Jae Kwang; Rao, J. N. K.; Wang, Zhonglei
作者单位：Iowa State University; Carleton University; Xiamen University; Xiamen University
摘要：Standard statistical methods without taking proper account of the complexity of a survey design can lead to erroneous inferences when applied to survey data due to unequal selection probabilities, clustering, and other design features. In particular, the Type I error rates of hypotheses tests using standard methods can be much larger than the nominal significance level. Methods incorporating design features in testing hypotheses have been proposed, including Wald tests and quasi-score tests th...
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作者：Nagler, Thomas; Vatter, Thibault
作者单位：University of Munich
摘要：Thanks to their ability to capture complex dependence structures, copulas are frequently used to glue random variables into a joint model with arbitrary marginal distributions. More recently, they have been applied to solve statistical learning problems such as regression or classification. Framing such approaches as solutions of estimating equations, we generalize them in a unified framework. We can then obtain simultaneous, coherent inferences across multiple regression-like problems. We der...
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作者：Zhao, Bingxin; Yang, Xiaochen; Zhu, Hongtu
作者单位：University of Pennsylvania; Purdue University System; Purdue University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：The aim of this article is to propose a novel method for estimating trans-ancestry genetic correlations in genome-wide association studies (GWAS) using genetically predicted observations. These correlations describe how genetic architecture of complex traits varies among populations. Our new estimator corrects for biases arising from prediction errors in high-dimensional weak GWAS signals, while addressing the ethnic diversity inherent in GWAS data, such as linkage disequilibrium (LD) differen...
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作者：Xu, Yuchen; Duker, Marie-Christine; Matteson, David S. S.
作者单位：Cornell University
摘要：This paper proposes novel methods to test for simultaneous diagonalization of possibly asymmetric matrices. Motivated by various applications, a two-sample test as well as a generalization for multiple matrices are proposed. A partial version of the test is also studied to check whether a partial set of eigenvectors is shared across samples. Additionally, a novel algorithm for the considered testing methods is introduced. Simulation studies demonstrate favorable performance for all designs. Fi...
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作者：Chen, Haoyu; Lu, Wenbin; Song, Rui; Ghosh, Pulak
作者单位：North Carolina State University; Indian Institute of Management (IIM System); Indian Institute of Management Bangalore; Indian Institute of Management (IIM System); Indian Institute of Management Bangalore
摘要：Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly. Recent work brings the discussion of machine learning fairness into the causal framework and elaborates on the concept of Counterfactual Fairness. In this paper, we develop the Fair Learning through dAta Preprocessing (FLAP) algorithm to learn counterfactually fair decisions from biased training data and formalize the conditions where di...
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作者：Liu, Changyu; Zhao, Xingqiu; Huang, Jian
作者单位：Hong Kong Polytechnic University
摘要：In this article, we consider the problem of hypothesis testing in high-dimensional single-index models. First, we study the feasibility of applying the classical F-test to a single-index model when the dimension of covariate vector and sample size are of the same order, and derive its asymptotic null distribution and asymptotic local power function. For the ultrahigh-dimensional single-index model, we construct F-statistics based on lower-dimensional random projections of the data, and establi...
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作者：Krivitsky, Pavel N.; Coletti, Pietro; Hens, Niel
作者单位：University of New South Wales Sydney; University of New South Wales Sydney; Hasselt University; University of Antwerp; University of Antwerp
摘要：This note provides correction to some numerical results in Krivitsky P. N., Coletti, P., and Hens, N. (2023), A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks, Journal of the American Statistical Association, 118, 2213-2224.
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作者：Fryzlewicz, Piotr
作者单位：University of London; London School Economics & Political Science; University of London; London School Economics & Political Science
摘要：We propose Narrowest Significance Pursuit (NSP), a general and flexible methodology for automatically detecting localized regions in data sequences which each must contain a change-point (understood as an abrupt change in the parameters of an underlying linear model), at a prescribed global significance level. NSP works with a wide range of distributional assumptions on the errors, and guarantees important stochastic bounds which directly yield exact desired coverage probabilities, regardless ...
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作者：Feng, Huijie; Duan, Jingyi; Ning, Yang; Zhao, Jiwei
作者单位：Cornell University; University of Wisconsin System; University of Wisconsin Madison
摘要：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 this parameter. Th...
