-
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...
-
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 ...
-
作者：Li, Xinran; Jiang, Bo; Liu, Jun S.
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Harvard University
摘要：We propose a kernel-based partial permutation test for checking the equality of functional relationship between response and covariates among different groups. The main idea, which is intuitive and easy to implement, is to keep the projections of the response vector Y on leading principle components of a kernel matrix fixed and permute Y's projections on the remaining principle components. The proposed test allows for different choices of kernels, corresponding to different classes of function...
-
作者：Jia, Yisu; Kechagias, Stefanos; Livsey, James; Lund, Robert; Pipiras, Vladas
作者单位：State University System of Florida; University of North Florida; SAS Institute Inc; University of California System; University of California Santa Cruz; University of North Carolina; University of North Carolina Chapel Hill
摘要：This article develops the theory and methods for modeling a stationary count time series via Gaussian transformations. The techniques use a latent Gaussian process and a distributional transformation to construct stationary series with very flexible correlation features that can have any prespecified marginal distribution, including the classical Poisson, generalized Poisson, negative binomial, and binomial structures. Gaussian pseudo-likelihood and implied Yule-Walker estimation paradigms, ba...
-
作者：Imai, Kosuke; Li, Michael Lingzhi
作者单位：Harvard University; Harvard University; Massachusetts Institute of Technology (MIT)
摘要：The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a target population. We propose a new evaluation metric, the population average prescriptive effect (PAPE). The PAPE compares the performance of ITR with that of non-individualized treatment rule, which randomly treats the same pro...
-
作者：Janson, Lucas
作者单位：Harvard University
-
作者：Claeskens, Gerda; Jansen, Maarten; Zhou, Jing
作者单位：KU Leuven; Universite Libre de Bruxelles; Universite Libre de Bruxelles; University of East Anglia; KU Leuven
-
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...
-
作者：Li, Runze; Xu, Kai; Zhou, Yeqing; Zhu, Liping
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Anhui Normal University; Tongji University; Renmin University of China; Renmin University of China
摘要：In this article, we test for the effects of high-dimensional covariates on the response. In many applications, different components of covariates usually exhibit various levels of variation, which is ubiquitous in high-dimensional data. To simultaneously accommodate such heteroscedasticity and high dimensionality, we propose a novel test based on an aggregation of the marginal cumulative covariances, requiring no prior information on the specific form of regression models. Our proposed test st...
-
作者：Castelletti, Federico; Peluso, Stefano
作者单位：Catholic University of the Sacred Heart; University of Milano-Bicocca
摘要：Gaussian Directed Acyclic Graphs (DAGs) represent a powerful tool for learning the network of dependencies among variables, a task which is of primary interest in many fields and specifically in biology. Different DAGs may encode equivalent conditional independence structures, implying limited ability, with observational data, to identify causal relations. In many contexts however, measurements are collected under heterogeneous settings where variables are subject to exogenous interventions. I...
