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作者：Bradic, Jelena; Ji, Weijie; Zhang, Yuqian
作者单位：University of California System; University of California San Diego; University of California System; University of California San Diego; Shanghai University of Finance & Economics; Renmin University of China
摘要：Estimating dynamic treatment effects is a crucial endeavor in causal inference, particularly when confronted with high-dimensional confounders. Doubly robust (DR) approaches have emerged as promising tools for estimating treatment effects due to their flexibility. However, we showcase that the traditional DR approaches that only focus on the DR representation of the expected outcomes may fall short of delivering optimal results. In this paper, we propose a novel DR representation for intermedi...
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作者：Escanciano, Juan Carlos
作者单位：Universidad Carlos III de Madrid
摘要：This paper proposes a Gaussian process (GP) approach for testing conditional moment restrictions. Tests are based on squared Neyman orthogonal function-parametric processes integrated with respect to a GP distribution. This methodology leads to a general unified framework of kernel-based tests having the following properties: (i) bootstrap tests are easy to implement in the presence of nuisance parameters (they are simple quadratic forms, and there is no need to reestimate the nuisance paramet...
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作者：Stephanovitch, Arthur; Aamari, Eddie; Levrard, Clement
作者单位：Centre National de la Recherche Scientifique (CNRS); Universite PSL; Ecole Normale Superieure (ENS); Centre National de la Recherche Scientifique (CNRS); Universite de Rennes
摘要：We provide nonasymptotic rates of convergence of the Wasserstein Generative Adversarial networks (WGAN) estimator. We build neural networks classes representing the generators and discriminators which yield a GAN that achieves the minimax optimal rate for estimating a certain probability measure mu with support in Rp. The probability mu is considered to be the push forward of the Lebesgue measure on the d-dimensional torus Td by a map g star : Td -> Rp of smoothness beta + 1. Measuring the err...
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作者：Yu, Haihan; Kaiser, Mark S.; Nordman, Daniel J.
作者单位：University of Rhode Island; Iowa State University
摘要：Frequency domain analysis of time series is often difficult, as periodogram-based statistics involve non-linear averages with complicated variances. Due to the latter, nonparametric approximations from resampling or empirical likelihood (EL) are useful. However, current versions of periodogram-based EL for time series are highly restricted: these are valid only for linear processes and for special parameters (i.e., ratios). For general frequency domain inference with stationary, weakly depende...
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作者：Schmidt-Hieber, Johannes; Vu, Don
作者单位：University of Twente; Vrije Universiteit Amsterdam
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作者：Berenfeld, Clement; Rosa, Paul; Rousseau, Judith
作者单位：University of Potsdam; University of Oxford
摘要：We study the Bayesian density estimation of data living in the offset of an unknown submanifold of the Euclidean space. In this perspective, we introduce a new notion of anisotropic H & ouml;lder for the underlying density and obtain posterior rates that are minimax optimal and adaptive to the regularity of the density, to the intrinsic dimension of the manifold, and to the size of the offset, provided that the latter is not too small-while still allowed to go to zero. Our Bayesian procedure, ...
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作者：Fischer, Lasse; Roig, Marta Bofill; Brannath, Werner
作者单位：University of Bremen; Medical University of Vienna
摘要：The closure principle is fundamental in multiple testing and has been used to derive many efficient procedures with familywise error rate control. However, it is often unsuitable for modern research, which involves flexible multiple testing settings where not all hypotheses are known at the beginning of the evaluation. In this paper, we focus on online multiple testing where a possibly infinite sequence of hypotheses is tested over time. At each step, it must be decided on the current hypothes...
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作者：Stankewitz, Bernhard
作者单位：Bocconi University
摘要：Increasingly high-dimensional data sets require that estimation methods do not only satisfy statistical guarantees but also remain computationally feasible. In this context, we consider L2-boosting via orthogonal matching pursuit in a high-dimensional linear model and analyze a data-driven early stopping time tau of the algorithm, which is sequential in the sense that its computation is based on the first tau iterations only. This approach is much less costly than established model selection c...
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作者：Zhao, Bingxin; Zheng, Shurong; Zhu, Hongtu
作者单位：University of Pennsylvania; Northeast Normal University - China; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：Genetic prediction holds immense promise for translating genetic discoveries into medical advances. As the high-dimensional covariance matrix (or the linkage disequilibrium (LD) pattern) of genetic variants often presents a block-diagonal structure, numerous methods account for the dependence among variants in predetermined local LD blocks. Moreover, due to privacy considerations and data protection concerns, genetic variant dependence in each LD block is typically estimated from external refe...
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作者：Chen, Weilin; Lam, Clifford
作者单位：University of London; London School Economics & Political Science
摘要：The idiosyncratic components of a tensor time series factor model can exhibit serial correlations, (e.g., finance or economic data), ruling out many ponents. While the traditional higher order orthogonal iteration (HOOI) is proved to be convergent to a set of factor loading matrices, the closeness of them to the true underlying factor loading matrices are in general not established, or only under i.i.d. Gaussian noises. Under the presence of serial and cross-correlations in the idiosyncratic c...
