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作者：Woody, S.; Padilla, O. H. M.; Scott, J. G.
作者单位：University of Texas System; University of Texas Austin; University of California System; University of California Los Angeles; University of Texas System; University of Texas Austin
摘要：Many recently developed Bayesian methods focus on sparse signal detection. However, much less work has been done on the natural follow-up question: how does one make valid inferences for the magnitude of those signals after selection? Ordinary Bayesian credible intervals suffer from selection bias, as do ordinary frequentist confidence intervals. Existing Bayesian methods for correcting this bias produce credible intervals with poor frequentist properties. Further, existing frequentist approac...
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作者：Zhao, Junlong; Liu, Xiumin; Wang, Hansheng; Leng, Chenlei
作者单位：Beijing Normal University; Peking University; University of Warwick
摘要：A problem of major interest in network data analysis is to explain the strength of connections using context information. To achieve this, we introduce a novel approach, called network-supervised dimension reduction, in which covariates are projected onto low-dimensional spaces to reveal the linkage pattern without assuming a model. We propose a new loss function for estimating the parameters in the resulting linear projection, based on the notion that closer proximity in the low-dimension pro...
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作者：Mammen, E.; Sperlich, S.
作者单位：Ruprecht Karls University Heidelberg; University of Geneva
摘要：We introduce bootstrap tests for semiparametric generalized structured models. These can be used for testing different kinds of model specifications like separability, functional forms and homogeneity of effects, or for performing variable selection in a large class of semiparametric models. The test statistics are based on the comparison of non- and semiparametric alternatives in which both the null hypothesis and the alternative are non- or semiparametric. All estimators are obtained by smoo...
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作者：van den Boom, W.; Reeves, G.; Dunson, D. B.
作者单位：Yale NUS College; National University of Singapore; Duke University
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作者：Zhao, Haibing
作者单位：Shanghai University of Finance & Economics
摘要：Post-selection inference on thousands of parameters has attracted considerable research interest in recent years. Specifically, Benjamini & Yekutieli (2005) considered constructing confidence intervals after selection. They proposed adjusting the confidence levels of marginal confidence intervals for the selected parameters to ensure control of the false coverage-statement rate. However, although Benjamini-Yekutieli confidence intervals are widely used, they are uniformly inflated. In this art...
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作者：Masoero, Lorenzo; Camerlenghi, Federico; Favaro, Stefano; Broderick, Tamara
作者单位：Massachusetts Institute of Technology (MIT); University of Milano-Bicocca; University of Turin
摘要：While the cost of sequencing genomes has decreased dramatically in recent years, this expense often remains nontrivial. Under a fixed budget, scientists face a natural trade-off between quantity and quality: spending resources to sequence a greater number of genomes or spending resources to sequence genomes with increased accuracy. Our goal is to find the optimal allocation of resources between quantity and quality. Optimizing resource allocation promises to reveal as many new variations in th...
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作者：Moon, Haeun; Chen, Kehui
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：We generalize the sign covariance introduced by Bergsma & Dassios (2014) to multivariate random variables and beyond. The new interpoint-ranking sign covariance is applicable to general types of random objects as long as a meaningful similarity measure can be defined, and it is shown to be zero if and only if the two random variables are independent. The test statistic is a $U$-statistic, whose large-sample behaviour guarantees that the proposed test is consistent against general types of alte...
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作者：Deresa, N. W.; Van Keilegom, I
作者单位：KU Leuven
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作者：Li, Yichao; Wang, Wenshuo; Deng, K. E.; Liu, Jun S.
作者单位：Tsinghua University; Harvard University
摘要：Sequential Monte Carlo algorithms are widely accepted as powerful computational tools for making inference with dynamical systems. A key step in sequential Monte Carlo is resampling, which plays the role of steering the algorithm towards the future dynamics. Several strategies have been used in practice, including multinomial resampling, residual resampling, optimal resampling, stratified resampling and optimal transport resampling. In one-dimensional cases, we show that optimal transport resa...
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作者：Duan, Rui; Ning, Yang; Chen, Yong
作者单位：Harvard University; Cornell University; University of Pennsylvania
摘要：In multicentre research, individual-level data are often protected against sharing across sites. To overcome the barrier of data sharing, many distributed algorithms, which only require sharing aggregated information, have been developed. The existing distributed algorithms usually assume the data are homogeneously distributed across sites. This assumption ignores the important fact that the data collected at different sites may come from various subpopulations and environments, which can lead...
