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作者：Camerlenghi, Federico; Favaro, Stefano; Naulet, Zacharie; Panero, Francesca
作者单位：University of Milano-Bicocca; University of Turin; Universite Paris Saclay; University of Oxford
摘要：Protection against disclosure is a legal and ethical obligation for agencies releasing microdata files for public use. Consider a microdata sample of size n from a finite population of size (n) over bar n = n + lambda n, with lambda > 0, such that each sample record contains two disjoint types of information: identifying categorical information and sensitive information. Any decision about releasing data is supported by the estimation of measures of disclosure risk, which are defined as discre...
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作者：Freise, Fritjof; Gaffke, Norbert; Schwabe, Rainer
作者单位：University of Veterinary Medicine Hannover; Otto von Guericke University
摘要：For a nonlinear regression model, the information matrices of designs depend on the parameter of the model. The adaptive Wynn algorithm for D-optimal design estimates the parameter at each step on the basis of the observed responses and employed design points so far, and selects the next design point as in the classicalWynn algorithm for D-optimal design. The name Wynn algorithm is in honor of Henry P. Wynn who established the latter classical algorithm in his 1970 paper (Ann. Math. Stat. 41 (...
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作者：Cai, Changxiao; Li, Gen; Chi, Yuejie; Poor, H. Vincent; Chen, Yuxin
作者单位：Princeton University; Tsinghua University; Carnegie Mellon University
摘要：This paper is concerned with estimating the column space of an unknown low-rank matrix A(star) is an element of R-d1xd2, given noisy and partial observations of its entries. There is no shortage of scenarios where the observations-while being too noisy to support faithful recovery of the entire matrix-still convey sufficient information to enable reliable estimation of the column space of interest. This is particularly evident and crucial for the highly unbalanced case where the column dimensi...
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作者：Nevo, Daniel; Lok, Judith J.; Spiegelman, Donna
作者单位：Tel Aviv University; Boston University; Yale University; Yale University
摘要：In Learn-As-you-GO (LAGO) adaptive studies, the intervention is a complex multicomponent package, and is adapted in stages during the study based on past outcome data. This design formalizes standard practice in public health intervention studies. An effective intervention package is sought, while minimizing intervention package cost. In LAGO study data, the interventions in later stages depend upon the outcomes in the previous stages, violating standard statistical theory. We develop an estim...
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作者：Goeman, Jelle J.; Hemerik, Jesse; Solari, Aldo
作者单位：Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC; University of Oslo; Wageningen University & Research; University of Milano-Bicocca
摘要：We consider the class of all multiple testing methods controlling tail probabilities of the false discovery proportion, either for one random set or simultaneously for many such sets. This class encompasses methods controlling familywise error rate, generalized familywise error rate, false discovery exceedance, joint error rate, simultaneous control of all false discovery proportions, and others, as well as gene set testing in genomics and cluster inference in neuroimaging. We show that all su...
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作者：Huetter, Jan-Christian; Rigollet, Philippe
作者单位：Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute; Massachusetts Institute of Technology (MIT)
摘要：Brenier's theorem is a cornerstone of optimal transport that guarantees the existence of an optimal transport map T between two probability distributions P and Q over R-d under certain regularity conditions. The main goal of this work is to establish the minimax estimation rates for such a transport map from data sampled from P and Q under additional smoothness assumptions on T. To achieve this goal, we develop an estimator based on the minimization of an empirical version of the semidual opti...
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作者：Xu, Min; Samworth, Richard J.
作者单位：Rutgers University System; Rutgers University New Brunswick; University of Cambridge
摘要：We tackle the problem of high-dimensional nonparametric density estimation by taking the class of log-concave densities on R-p and incorporating within it symmetry assumptions, which facilitate scalable estimation algorithms and can mitigate the curse of dimensionality. Our main symmetry assumption is that the super-level sets of the density are K-homothetic (i.e., scalar multiples of a convex body K subset of R-p). When K is known, we prove that the K-homothetic log-concave maximum likelihood...
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作者：Savje, Fredrik; Aronow, Peter M.; Hudgens, Michael G.
作者单位：Yale University; Yale University; University of North Carolina; University of North Carolina Chapel Hill
摘要：We investigate large-sample properties of treatment effect estimators under unknown interference in randomized experiments. The inferential target is a generalization of the average treatment effect estimand that marginalizes over potential spillover effects. We show that estimators commonly used to estimate treatment effects under no interference are consistent for the generalized estimand for several common experimental designs under limited but otherwise arbitrary and unknown interference. ...
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作者：Chandler, Gabriel; Polonik, Wolfgang
作者单位：Claremont Colleges; Pomona College; University of California System; University of California Davis
摘要：A method for extracting multiscale geometric features from a data cloud is proposed and analyzed. Based on geometric considerations, we map each pair of data points into a real-valued feature function defined on the unit interval. Further statistical analysis is then based on the collection of feature functions. The potential of the method is illustrated by different applications, including classification and anomaly detection. Connections to other concepts, such as random set theory, localize...
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作者：Chakraborty, Moumita; Ghosal, Subhashis
作者单位：North Carolina State University
摘要：For nonparametric univariate regression under a monotonicity constraint on the regression function f, we study the coverage of a Bayesian credible interval for f (x(0)), where x(0) is an interior point. Analysis of the posterior becomes a lot more tractable by considering a projection-posterior distribution based on a finite random series of step functions with normal basis coefficients as a prior for f. A sample f from the resulting conjugate posterior distribution is projected on the space o...
