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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：Yu, Cheng; Li, Dong; Jiang, Feiyu; Zhu, Ke
作者单位：Tsinghua University; Fudan University; University of Hong Kong
摘要：Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroscedasticity that is often observed in economic and financial data. To address this gap, we propose a novel matrix generalized autoregressive conditional heteroscedasticity (GARCH) model to capture the dynamics of conditional row and column covariance matrices of matrix time series. The key innovation of the matrix GARCH model is the use of a univariate GARC...
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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：Wong, Raymond K. W.; Zubizarreta, Jose R.; Stuart, Elizabeth A.; Small, Dylan S.; Rosenbaum, Paul R.
作者单位：Texas A&M University System; Texas A&M University College Station
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作者：Seo, Insuk
作者单位：Seoul National University (SNU)
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作者：Zheng, Jiajing; Wu, Jiaxi; D'Amour, Alexander; Franks, Alexander
作者单位：University of California System; University of California Santa Barbara; Alphabet Inc.; Google Incorporated
摘要：In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-outcome setting can be leveraged to strengthen causal conclusions beyond what can be achieved from analyzing individual outcomes in isolation. We argue that it is often reasonable to make a shared confounding assumption, under which residual dependence amongst outcomes can be used to simplify and sharpen sensitivity a...
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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...
