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作者：Chen, Yuexin; Zhu, Lixing; Xu, Wangli
作者单位：Renmin University of China; Renmin University of China; Beijing Normal University
摘要：This article proposes a calibrated empirical likelihood test for ultra-high dimensional means that incorporates multiple projections. Under weak moment conditions on the distributions of data, we analyse all possible asymptotic distributions of the proposed test statistic in different scenarios. To determine the critical value and enhance test power, we employ the random symmetrization method based on the group of sign flips and use multiple selected projections. The test can still maintain th...
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作者：Yang, Shu; Ding, Peng
作者单位：North Carolina State University; University of California System; University of California Berkeley
摘要：Rejective sampling improves design and estimation efficiency of single-phase sampling when auxiliary information in a finite population is available. When such auxiliary information is unavailable, we propose to use two-phase rejective sampling (TPRS), which involves measuring auxiliary variables for the sample of units in the first phase, followed by the implementation of rejective sampling for the outcome in the second phase. We explore the asymptotic design properties of double expansion an...
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作者：Chen, Haolin; Dette, Holger; Yu, Jun
作者单位：Beijing Institute of Technology; Ruhr University Bochum
摘要：Subsampling is one of the popular methods to balance statistical efficiency and computational efficiency in the big data era. Most approaches aim to select informative or representative sample points to achieve good overall information of the full data. The present work takes the view that sampling techniques are recommended for the region we focus on and summary measures are enough to collect the information for the rest according to a well-designed data partitioning. We propose a subsampling...
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作者：Jacobson, Tate
作者单位：Oregon State University
摘要：Partial penalized tests provide flexible approaches to testing linear hypotheses in high-dimensional generalized linear models. However, because the estimators used in these tests are local minimizers of potentially nonconvex folded-concave penalized objectives, the solutions one computes in practice may not coincide with the unknown local minima for which we have nice theoretical guarantees. To close this gap between theory and computation, we introduce local linear approximation (LLA) algori...
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作者：Borgonovo, Emanuele; Figalli, Alessio; Ghosal, Promit; Plischke, Elmar; Savare, Giuseppe
作者单位：Bocconi University; Bocconi University; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Chicago; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
摘要：Recent investigations on the measures of statistical association highlight essential properties such as zero-independence (the measure is zero if and only if the random variables are independent), monotonicity under information refinement, and max-functionality (the measure of association is maximal if and only if we are in the presence of a deterministic (noiseless) dependence). An open question concerns the reasons why measures of statistical associations satisfy one or more of those propert...
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作者：Tan, Linda S. L.
作者单位：National University of Singapore
摘要：Natural gradients can improve convergence in stochastic variational inference significantly but inverting the Fisher information matrix is daunting in high dimensions. Moreover, in Gaussian variational approximation, natural gradient updates of the precision matrix do not ensure positive definiteness. To tackle this issue, we derive analytic natural gradient updates of the Cholesky factor of the covariance or precision matrix and consider sparsity constraints representing different posterior c...
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作者：Jin, Ying; Ren, Zhimei
作者单位：Harvard University; University of Pennsylvania
摘要：Conformal prediction builds marginally valid prediction intervals that cover the unknown outcome of a randomly drawn test point with a prescribed probability. However, in practice, data-driven methods are often used to identify specific test unit(s) of interest, requiring uncertainty quantification tailored to these focal units. In such cases, marginally valid conformal prediction intervals may fail to provide valid coverage for the focal unit(s) due to selection bias. This article presents a ...
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作者：Fischer, Lasse; Ramdas, Aaditya
作者单位：University of Bremen; Carnegie Mellon University
摘要：In a Monte Carlo test, the observed dataset is fixed, and several resampled or permuted versions of the dataset are generated in order to test a null hypothesis that the original dataset is exchangeable with the resampled/permuted ones. Sequential Monte Carlo tests aim to save computational resources by generating these additional datasets sequentially one by one and potentially stopping early. While earlier tests yield valid inference at a particular prespecified stopping rule, our work devel...
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作者：Rosenbaum, Paul R.
作者单位：University of Pennsylvania
摘要：In an observational block design, there are I blocks of J individuals, typically with one treated individual and J-1 controls; however, unlike a randomized block design, individuals were not randomly assigned to treatment or control. To be convincing, an observational block design must demonstrate that an ostensible treatment effect is not actually a consequence of small or moderate unmeasured biases of treatment assignment in the absence of a treatment effect. It is known that weighting to ig...
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作者：Borgonovo, Emanuele; Figalli, Alessio; Ghosal, Promit; Plischke, Elmar; Savare, Giuseppe
作者单位：Bocconi University; Bocconi University; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Chicago; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
