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作者：Guggisberg, Michael
摘要：This article presents a Bayesian approach to multiple-output quantile regression. The prior can be elicited as ex-ante knowledge of the distance of the tau-Tukey depth contour to the Tukey median, the first prior of its kind. The parametric model is proven to be consistent and a procedure to obtain confidence intervals is proposed. A proposal for nonparametric multiple-output regression is also presented. These results add to the literature of misspecified Bayesian modeling, consistency, and p...
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作者：Zhang, Yichi; Shen, Weining; Kong, Dehan
作者单位：North Carolina State University; University of California System; University of California Irvine; University of Toronto
摘要：Covariance estimation for matrix-valued data has received an increasing interest in applications. Unlike previous works that rely heavily on matrix normal distribution assumption and the requirement of fixed matrix size, we propose a class of distribution-free regularized covariance estimation methods for high-dimensional matrix data under a separability condition and a bandable covariance structure. Under these conditions, the original covariance matrix is decomposed into a Kronecker product ...
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作者：Cerovecki, Clement; Characiejus, Vaidotas; Hoermann, Siegfried
作者单位：KU Leuven; Universite Libre de Bruxelles; University of Southern Denmark; Graz University of Technology
摘要：We study the periodogram operator of a sequence of functional data. Using recent advances in Gaussian approximation theory, we derive the asymptotic distribution of the maximum norm over all fundamental frequencies. We consider the case where the noise variables are independent and then generalize our results to functional linear processes. Our theory can be used for detecting periodic signals in functional time series when the length of the period is unknown. We demonstrate the proposed metho...
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作者：Vega Yon, George G.
作者单位：Utah System of Higher Education; University of Utah
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作者：Keret, Nir; Gorfine, Malka
作者单位：Tel Aviv University
摘要：Massive sized survival datasets become increasingly prevalent with the development of the healthcare industry, and pose computational challenges unprecedented in traditional survival analysis use cases. In this work we analyze the UK-biobank colorectal cancer data with genetic and environmental risk factors, including a time-dependent coefficient, which transforms the dataset into pseudo-observation form, thus, critically inflating its size. A popular way for coping with massive datasets is do...
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作者：Perreault, Samuel; Neslehova, Johanna G.; Duchesne, Thierry
作者单位：University of Toronto; McGill University; Laval University
摘要：Joint modeling of a large number of variables often requires dimension reduction strategies that lead to structural assumptions of the underlying correlation matrix, such as equal pair-wise correlations within subsets of variables. The underlying correlation matrix is thus of interest for both model specification and model validation. In this article, we develop tests of the hypothesis that the entries of the Kendall rank correlation matrix are linear combinations of a smaller number of parame...
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作者：Panigrahi, Snigdha; Taylor, Jonathan
作者单位：University of Michigan System; University of Michigan; Stanford University
摘要：Several strategies have been developed recently to ensure valid inference after model selection; some of these are easy to compute, while others fare better in terms of inferential power. In this article, we consider a selective inference framework for Gaussian data. We propose a new method for inference through approximate maximum likelihood estimation. Our goal is to: (a) achieve better inferential power with the aid of randomization, (b) bypass expensive MCMC sampling from exact conditional...
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作者：Dorn, Jacob; Guo, Kevin
作者单位：Princeton University; Stanford University
摘要：Inverse propensity weighting (IPW) is a popular method for estimating treatment effects from observational data. However, its correctness relies on the untestable (and frequently implausible) assumption that all confounders have been measured. This article introduces a robust sensitivity analysis for IPW that estimates the range of treatment effects compatible with a given amount of unobserved confounding. The estimated range converges to the narrowest possible interval (under the given assump...
