6期

最新文章

• GAUSSIAN PROCESS REGRESSION IN THE FLAT LIMIT AI翻译
  作者：Barthelme, Simon; Amblard, Pierre-Oliviera; Remblay, Nicolas; Usevich, Konstantin
  作者单位：Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Universite de Lorraine; Centre National de la Recherche Scientifique (CNRS)
  摘要：Gaussian process (GP) regression is a fundamental tool in Bayesian statistics. It is also known as kriging and is the Bayesian counterpart to the frequentist kernel ridge regression. Most of the theoretical work on GP regression has focused on a large-n asymptotics, characterising the behaviour of GP regression as the amount of data increases. Fixed-sample analysis is much more difficult outside of simple cases, such as locations on a regular grid.In this work, we perform a fixed-sample analys...
• S-ESTIMATION IN LINEAR MODELS WITH STRUCTURED COVARIANCE MATRICES AI翻译
  作者：Lopuhaa, Hendrik Paul; Gares, Valerie; Ruiz-Gazen, Anne
  作者单位：Delft University of Technology; Universite de Rennes; Universite de Toulouse; Universite Toulouse 1 Capitole; Toulouse School of Economics
  摘要：We provide a unified approach to S-estimation in balanced linear models with structured covariance matrices. Of main interest are S-estimators for linear mixed effects models, but our approach also includes S-estimators in several other standard multivariate models, such as multiple regression, multivariate regression and multivariate location and scatter. We provide sufficient conditions for the existence of S-functionals and S-estimators, establish asymptotic properties such as consistency a...
• CARVING MODEL-FREE INFERENCE AI翻译
  作者：Panigrahi, Snigdha
  作者单位：University of Michigan System; University of Michigan
  摘要：Complex studies involve many steps. Selecting promising findings based on pilot data is a first step. As more observations are collected, the investigator must decide how to combine the new data with the pilot data to construct valid selective inference. Carving, introduced by Fithian, Sun and Taylor (2014), enables the reuse of pilot data during selective inference and accounts for overoptimism from the selection process. However, currently, carving is only justified for parametric models suc...
• ADJUSTED CHI-SQUARE TEST FOR DEGREE-CORRECTED BLOCK MODELS AI翻译
  作者：Zhang, Linfan; Amini, Arash a.
  作者单位：University of California System; University of California Los Angeles
  摘要：We propose a goodness-of-fit test for degree-corrected stochastic block models (DCSBM). The test is based on an adjusted chi-square statistic for measuring equality of means among groups of n multinomial distributions with d(1), ... , d(n) observations. In the context of network models, the num-ber of multinomials, n, grows much faster than the number of observations, di, corresponding to the degree of node i, hence the setting deviates from classical asymptotics. We show that a simple adjustm...
• EFFICIENT SHAPE-CONSTRAINED INFERENCE FOR THE AUTOCOVARIANCE SEQUENCE FROM A REVERSIBLE MARKOV CHAIN AI翻译
  作者：Berg, Stephen; Song, Hyebin
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：In this paper, we study the problem of estimating the autocovariance sequence resulting from a reversible Markov chain. A motivating application for studying this problem is the estimation of the asymptotic variance in central limit theorems for Markov chains. We propose a novel shape-constrained estimator of the autocovariance sequence, which is based on the key observation that the representability of the autocovariance sequence as a moment sequence imposes certain shape constraints. We exam...
• OPTIMAL SUBGROUP SELECTION AI翻译
  作者：Reeve, Henry W. J.; Cannings, Timothy I.; Samworth, Richard J.
  作者单位：University of Bristol; University of Edinburgh; University of Edinburgh; Heriot Watt University; University of Cambridge
  摘要：In clinical trials and other applications, we often see regions of the feature space that appear to exhibit interesting behaviour, but it is unclear whether these observed phenomena are reflected at the population level. Fo-cusing on a regression setting, we consider the subgroup selection challenge of identifying a region of the feature space on which the regression func-tion exceeds a pre-determined threshold. We formulate the problem as one of constrained optimisation, where we seek a low-c...
• NONLINEAR NETWORK AUTOREGRESSION AI翻译
  作者：Armillotta, Mirko; Fokianos, Konstantinos
  作者单位：Vrije Universiteit Amsterdam; University of Cyprus
  摘要：We study general nonlinear models for time series networks of integer and continuous-valued data. The vector of high-dimensional responses, measured on the nodes of a known network, is regressed nonlinearly on its lagged value and on lagged values of the neighboring nodes by employing a smooth link function. We study stability conditions for such multivariate process and develop quasi-maximum likelihood inference when the network dimension is increasing. In addition, we study linearity score t...
• LOCAL WHITTLE ESTIMATION OF HIGH-DIMENSIONAL LONG-RUN VARIANCE AND PRECISION MATRICES AI翻译
  作者：Baek, Changryong; Duker, Marie-christine; Pipiras, Vladas
  作者单位：Sungkyunkwan University (SKKU); Cornell University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
  摘要：This work develops nonasymptotic theory for estimation of the longrun variance matrix and its inverse, the so-called precision matrix, for highdimensional time series under general assumptions on the dependence structure including long-range dependence. The estimation involves shrinkage techniques, which are thresholding and penalizing versions of the classical multivariate local Whittle estimator. The results ensure consistent estimation in a double asymptotic regime where the number of compo...
• PROJECTIVE, SPARSE AND LEARNABLE LATENT POSITION NETWORK MODELS AI翻译
  作者：Spencer, Neil A.; Shalizi, Cosma Rohilla
  作者单位：University of Connecticut; Carnegie Mellon University
  摘要：When modeling network data using a latent position model, it is typical to assume that the nodes' positions are independently and identically distributed. However, this assumption implies the average node degree grows linearly with the number of nodes, which is inappropriate when the graph is thought to be sparse. We propose an alternative assumption-that the latent positions are generated according to a Poisson point process-and show that it is compatible with various levels of sparsity. Unli...
• TESTING NONPARAMETRIC SHAPE RESTRICTIONS AI翻译
  作者：Komarova, Tatiana; Hidalgo, Javier
  作者单位：University of Manchester; University of London; London School Economics & Political Science
  摘要：We describe and examine a test for a general class of shape constraints, such as signs of derivatives, U-shape, quasi-convexity, log-convexity, among others, in a nonparametric framework using partial sums empirical processes. We show that, after a suitable transformation, its asymptotic distribution is a functional of a Brownian motion index by the c.d.f. of the regressor. As a result, the test is distribution-free and critical values are readily available. However, due to the possible poor a...