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• Nonparametric regression with adaptive truncation via a convex hierarchical penalty AI翻译
  作者：Haris, Asad; Shojaie, Ali; Simon, Noah
  作者单位：University of Washington; University of Washington Seattle
  摘要：We consider the problem of nonparametric regression with a potentially large number of covariates. We propose a convex, penalized estimation framework that is particularly well suited to high-dimensional sparse additive models and combines the appealing features of finite basis representation and smoothing penalties. In the case of additive models, a finite basis representation provides a parsimonious representation for fitted functions but is not adaptive when component functions possess diff...
• Classification of functional fragments by regularized linear classifiers with domain selection AI翻译
  作者：Kraus, David; Stefanucci, Marco
  作者单位：Masaryk University; Sapienza University Rome
  摘要：We consider classification of functional data into two groups by linear classifiers based on one-dimensional projections of functions. We reformulate the task of finding the best classifier as an optimization problem and solve it by the conjugate gradient method with early stopping, the principal component method, and the ridge method. We study the empirical version with finite training samples consisting of incomplete functions observed on different subsets of the domain and show that the opt...
• Extremal behaviour of aggregated data with an application to downscaling AI翻译
  作者：Engelke, Sebastian; De Fondeville, Raphael; Oesting, Marco
  作者单位：University of Geneva; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Universitat Siegen
  摘要：The distribution of spatially aggregated data from a stochastic process may exhibit tail behaviour different from that of its marginal distributions. For a large class of aggregating functionals we introduce the -extremal coefficient, which quantifies this difference as a function of the extremal spatial dependence in . We also obtain the joint extremal dependence for multiple aggregation functionals applied to the same process. Formulae for the -extremal coefficients and multivariate dependen...
• Signal-plus-noise matrix models: eigenvector deviations and fluctuations AI翻译
  作者：Cape, J.; Tang, M.; Priebe, C. E.
  作者单位：Johns Hopkins University
  摘要：Estimating eigenvectors and low-dimensional subspaces is of central importance for numerous problems in statistics, computer science and applied mathematics. In this paper we characterize the behaviour of perturbed eigenvectors for a range of signal-plus-noise matrix models encountered in statistical and random-matrix-theoretic settings. We establish both first-order approximation results, i.e., sharp deviations, and second-order distributional limit theory, i.e., fluctuations. The concise met...
• Discussion of Gene hunting with hidden Markov model knockoffs' AI翻译
  作者：Marchini, J. L.
  作者单位：Regeneron
• Testing for independence in arbitrary distributions AI翻译
  作者：Genest, C.; Neslehova, J. G.; Remillard, B.; Murphy, O. A.
  作者单位：McGill University; Universite de Montreal; HEC Montreal
  摘要：Statistics are proposed for testing the hypothesis that arbitrary random variables are mutually independent. The tests are consistent and well behaved for any marginal distributions; they can be used, for example, for contingency tables which are sparse or whose dimension depends on the sample size, as well as for mixed data. No regularity conditions, data jittering, or binning mechanisms are required. The statistics are rank-based functionals of Cramer-von Mises type whose asymptotic behaviou...
• Gene hunting with hidden Markov model knockoffs AI翻译
  作者：Sesia, M.; Sabatti, C.; Candes, E. J.
  作者单位：Stanford University
  摘要：Modern scientific studies often require the identification of a subset of explanatory variables. Several statistical methods have been developed to automate this task, and the framework of knockoffs has been proposed as a general solution for variable selection under rigorous Type I error control, without relying on strong modelling assumptions. In this paper, we extend the methodology of knockoffs to problems where the distribution of the covariates can be described by a hidden Markov model. ...
• Semiparametric inference for the dominance index under the density ratio model AI翻译
  作者：Zhuang, W. W.; Hu, B. Y.; Chen, J.
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of British Columbia; Yunnan University
  摘要：An important and often discussed research problem in statistics is how to compare several populations; examples arise in medical science, engineering, finance and other fields. Often population means or medians are compared. However, one population may have a higher mean income, for example, because of a small number of super-rich individuals; the mean therefore may not reflect the wealth of the general population. Instead, an index of the degree of stochastic dominance of one population over ...
• Discussion of Gene hunting with hidden Markov model knockoffs' AI翻译
  作者：Bottolo, L.; Richardson, S.
  作者单位：University of Cambridge; MRC Biostatistics Unit; University of Cambridge
• Goodness-of-fit tests for the cure rate in a mixture cure model AI翻译
  作者：Mueller, U. U.; Van Keilegom, I.
  作者单位：Texas A&M University System; Texas A&M University College Station; KU Leuven
  摘要：We consider models for time-to-event data that allow that an event, e.g., a relapse of a disease, never occurs for a certain percentage of the population, called the cure rate. We suppose that these data are subject to random right censoring and we model the data using a mixture cure model, in which the survival function of the uncured subjects is left unspecified. The aim is to test whether the cure rate , as a function of the covariates, satisfies a certain parametric model. To do so, we pro...