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作者：Zhang, Yangfan; Wang, Runmin; Shao, Xiaofeng
作者单位：Texas A&M University System; Texas A&M University College Station; University of Illinois System; University of Illinois Urbana-Champaign
摘要：In this article, we propose a class of L-q -norm based U-statistics for a family of global testing problems related to high-dimensional data. This includes testing of mean vector and its spatial sign, simultaneous testing of linear model coefficients, and testing of component-wise independence for high-dimensional observations, among others. Under the null hypothesis, we derive asymptotic normality and independence between L-q -norm based U-statistics for several qs under mild moment and cumul...
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作者：Kundig, Pascal; Sigrist, Fabio
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Basel
摘要：Latent Gaussian process (GP) models are flexible probabilistic nonparametric function models. Vecchia approximations are accurate approximations for GPs to overcome computational bottlenecks for large data, and the Laplace approximation is a fast method with asymptotic convergence guarantees to approximate marginal likelihoods and posterior predictive distributions for non-Gaussian likelihoods. Unfortunately, the computational complexity of combined Vecchia-Laplace approximations grows faster ...
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作者：Chen, Yan; Lin, Hongmei; Wang, Xueqin; Wen, Canhong
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Shanghai University of International Business & Economics; Chinese Academy of Sciences; University of Science & Technology of China, CAS
摘要：Our proposed approach addresses the challenges associated with nonparametric two-sample testing for densely measured functional data. These challenges stem from the high dimensionality of data and the nature of the observation scheme. We introduce a novel metric concept for random functions known as Grothendieck's divergence to overcome these challenges, which satisfies the homogeneity-zero equivalence property. Our approach uses a pre-smoothing technique on densely measured functional data an...
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作者：Zheng, Zemin; Zhou, Xin; Fan, Yingying; Lv, Jinchi
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Southern California
摘要：Multi-task learning is a widely used technique for harnessing information from various tasks. Recently, the sparse orthogonal factor regression (SOFAR) framework, based on the sparse singular value decomposition (SVD) within the coefficient matrix, was introduced for interpretable multi-task learning, enabling the discovery of meaningful latent feature-response association networks across different layers. However, conducting precise inference on the latent factor matrices has remained challen...
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作者：Li, Kevin; Mak, Simon; Paquet, J. -F; Bass, Steffen A.
作者单位：Duke University; Vanderbilt University; Vanderbilt University; Duke University
摘要：The Quark-Gluon Plasma (QGP) is a unique phase of nuclear matter, theorized to have filled the Universe shortly after the Big Bang. A critical challenge in studying the QGP is that, to reconcile experimental observables with theoretical parameters, one requires many simulation runs of a complex physics model over a high-dimensional parameter space. Each run is computationally expensive, requiring thousands of CPU hours, thus limiting physicists to only several hundred runs. Given limited train...
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作者：Rambachan, Ashesh; Roth, Jonathan
作者单位：Massachusetts Institute of Technology (MIT); Brown University
摘要：Design-based frameworks of uncertainty are frequently used in settings where the treatment is (conditionally) randomly assigned. This article develops a design-based framework suitable for analyzing quasi-experimental settings in the social sciences, in which the treatment assignment can be viewed as the realization of some stochastic process but there is concern about unobserved selection into treatment. In our framework, treatments are stochastic, but units may differ in their probabilities ...
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作者：Heng, Siyu; Zhang, Jiawei; Feng, Yang
作者单位：New York University; New York University; University of Chicago
摘要：Design-based causal inference, also known as randomization-based or finite-population causal inference, is one of the most widely used causal inference frameworks, largely due to the merit that its validity can be guaranteed by study design (e.g., randomized experiments) and does not require assuming specific outcome-generating distributions or super-population models. Despite its advantages, design-based causal inference can still suffer from other issues, among which outcome missingness is a...
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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：The spectral density function can play a key role in time series analysis, where nonparametric interval estimation of the spectral density is a fundamental issue. However, the prevailing pointwise interval methods for spectral densities, including Chi-square approximation and frequency domain bootstrap (FDB), can be misleading in practice, perhaps more so than appreciated, as confidence intervals often exhibit low coverage accuracy as well as high sensitivity to tuning parameters. To provide a...
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作者：Ignatiadis, Nikolaos; Sun, Dennis L.
作者单位：University of Chicago; Stanford University
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作者：Zuo, Shuozhi; Ghosh, Debashis; Ding, Peng; Yang, Fan
作者单位：Colorado School of Public Health; University of California System; University of California Berkeley; Tsinghua University; Yanqi Lake Beijing Institute of Mathematical Sciences & Applications
摘要：Mediation analysis is widely used for investigating direct and indirect causal pathways through which an effect arises. However, many mediation analysis studies are challenged by missingness in the mediator and outcome. In general, when the mediator and outcome are missing not at random, the direct and indirect effects are not identifiable without further assumptions. We study the identifiability of the direct and indirect effects under some interpretable mechanisms that allow for missing not ...
