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作者：Feng, Long; Jiang, Tiefeng; Liu, Binghui; Xiong, Wei
作者单位：Nankai University; Nankai University; University of Minnesota System; University of Minnesota Twin Cities; Northeast Normal University - China; Northeast Normal University - China; University of International Business & Economics
摘要：We consider a testing problem for cross-sectional independence for high-dimensional panel data, where the number of cross-sectional units is potentially much larger than the number of observations. The cross-sectional independence is described through linear regression models. We study three tests named the sum, the max and the max-sum tests, where the latter two are new. The sum test is initially proposed by Breusch and Pagan (1980). We design the max and sum tests for sparse and nonsparse co...
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作者：He, Xuming; Pan, Xiaoou; Tan, Kean Ming; Zhou, Wen-Xin
作者单位：University of Michigan System; University of Michigan; University of California System; University of California San Diego
摘要：Censored quantile regression (CQR) has become a valuable tool to study the heterogeneous association between a possibly censored outcome and a set of covariates, yet computation and statistical inference for CQR have remained a challenge for large-scale data with many covariates. In this paper, we focus on a smoothed martingale-based sequential estimating equations approach, to which scalable gradient-based algorithms can be applied. Theoretically, we provide a unified analysis of the smoothed...
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作者：Huang, Yijian; Sanda, Martin G.
作者单位：Emory University; Emory University
摘要：Multiple biomarkers are often combined to improve disease diagnosis. The uniformly optimal combination, that is, with respect to all reasonable performance metrics, unfortunately requires excessive distributional modeling, to which the estimation can be sensitive. An alternative strategy is rather to pursue local optimality with respect to a specific performance metric. Nevertheless, existing methods may not target clinical utility of the intended medical test, which usually needs to operate a...
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作者：Wei, Yun; Nguyen, XuanLong
作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：Mixtures of product distributions are a powerful device for learning about heterogeneity within data populations. In this class of latent structure models, de Finetti's mixing measure plays the central role for describing the uncertainty about the latent parameters representing heterogeneity. In this paper, posterior contraction theorems for de Finetti's mixing measure arising from finite mixtures of product distributions will be established; under the setting the number of exchangeable sequen...
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作者：Padoan, Simone A.; Rizzelli, Stefano
作者单位：Bocconi University; Catholic University of the Sacred Heart
摘要：Predicting extreme events is important in many applications in risk analysis. Extreme-value theory suggests modelling extremes by max-stable distributions. The Bayesian approach provides a natural framework for statistical prediction. Although various Bayesian inferential procedures have been proposed in the literature of univariate extremes and some for multivariate extremes, the study of their asymptotic properties has been left largely untouched. In this paper we focus on a semiparametric B...
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作者：Aswani, Anil; Olfat, Matt
作者单位：University of California System; University of California Berkeley
摘要：Data-driven decision making has drawn scrutiny from policy makers due to fears of potential discrimination, and a growing literature has begun to develop fair statistical techniques. However, these techniques are often spe-cialized to one model context and based on ad hoc arguments, which makes it difficult to perform theoretical analysis. This paper develops an optimization hierarchy, which is a sequence of optimization problems with an increasing number of constraints, for fair statistical d...
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作者：Koltchinskii, Vladimir
作者单位：University System of Georgia; Georgia Institute of Technology
摘要：Let X-(n) be an observation sampled from a distribution P(theta)((n) )with an unknown parameter theta, theta being a vector in a Banach space E (most often, a high-dimensional space of dimension d). We study the problem of estimation of f (theta) for a functional f : E bar right arrow R of some smoothness s > 0 based on an observation X-(n) similar to P-theta((n)). Assuming that there exists an estimator (theta) over cap (n) = (theta) over cap (n) (X-(n)) of parameter theta such that root n((t...
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作者：Liang, Tengyuan; Sur, Pragya
作者单位：University of Chicago; Harvard University
摘要：This paper establishes a precise high-dimensional asymptotic theory for boosting on separable data, taking statistical and computational perspectives. We consider a high-dimensional setting where the number of features (weak learners) p scales with the sample size n, in an overparametrized regime. Under a class of statistical models, we provide an exact analysis of the generalization error of boosting when the algorithm interpolates the training data and maximizes the empirical l(1)-margin. Fu...
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作者：Bao, Zhigang; Ding, Xiucai; Wang, Jingming; Wang, Ke
作者单位：Hong Kong University of Science & Technology; University of California System; University of California Davis
摘要：In this paper, we study the asymptotic behavior of the extreme eigenvalues and eigenvectors of the high-dimensional spiked sample covariance matrices, in the supercritical case when a reliable detection of spikes is possible. In particular, we derive the joint distribution of the extreme eigenvalues and the generalized components of the associated eigenvectors, that is, the projections of the eigenvectors onto arbitrary given direction, assuming that the dimension and sample size are comparabl...
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作者：Aeckerle-willems, Cathrine; Strauch, Claudia
作者单位：University of Mannheim; Aarhus University
摘要：We consider the question of estimating the drift for a large class of er-godic multivariate and possibly nonreversible diffusion processes, based on continuous observations, in sup-norm loss. Nonparametric classes of smooth functions of unknown order are considered, and we suggest an adaptive ap-proach which allows to construct drift estimators attaining optimal sup-norm rates of convergence. Reversibility structures and related functional inequali-ties are known to be key tools for these esti...
