6期

最新文章

• OPTIMAL ADAPTIVITY OF SIGNED-POLYGON STATISTICS FOR NETWORK TESTING AI翻译
  作者：Jin, Jiashun; Ke, Zheng Tracy; Luo, Shengming
  作者单位：Carnegie Mellon University; Harvard University
  摘要：Given a symmetric social network, we are interested in testing whether it has only one community or multiple communities. The desired tests should (a) accommodate severe degree heterogeneity, (b) accommodate mixed memberships, (c) have a tractable null distribution and (d) adapt automatically to different levels of sparsity, and achieve the optimal phase diagram. How to find such a test is a challenging problem. We propose the Signed Polygon as a class of new tests. Fixing m >= 3, for each m-g...
• ARE DEVIATIONS IN A GRADUALLY VARYING MEAN RELEVANT? A TESTING APPROACH BASED ON SUP-NORM ESTIMATORS AI翻译
  作者：Buecher, Axel; Dette, Holger; Heinrichs, Florian
  作者单位：Heinrich Heine University Dusseldorf; Ruhr University Bochum
  摘要：Classical change point analysis aims at (1) detecting abrupt changes in the mean of a possibly nonstationary time series and at (2) identifying regions where the mean exhibits a piecewise constant behavior. In many applications however, it is more reasonable to assume that the mean changes gradually in a smooth way. Those gradual changes may either be nonrelevant (i.e., small), or relevant for a specific problem at hand, and the present paper presents statistical methodology to detect the latt...
• POSTERIOR ANALYSIS OF n IN THE BINOMIAL (n, p) PROBLEM WITH BOTH PARAMETERS UNKNOWN-WITH APPLICATIONS TO QUANTITATIVE NANOSCOPY AI翻译
  作者：Schmidt-Hieber, Johannes; Schneider, Laura Fee; Staudt, Thomas; Krajina, Andrea; Aspelmeier, Timo; Munk, Axel
  作者单位：University of Twente; University of Gottingen; Max Planck Society; University of Gottingen; UNIVERSITY GOTTINGEN HOSPITAL
  摘要：Estimation of the population size n from k i.i.d. binomial observations with unknown success probability p is relevant to a multitude of applications and has a long history. Without additional prior information this is a notoriously difficult task when p becomes small, and the Bayesian approach becomes particularly useful. For a large class of priors, we establish posterior contraction and a Bernstein-von Mises type theorem in a setting where p -> 0 and n -> infinity as k -> infinity. Furtherm...
• AN ASYMPTOTIC TEST FOR CONSTANCY OF THE VARIANCE UNDER SHORT-RANGE DEPENDENCE AI翻译
  作者：Schmidt, Sara K.; Wornowizki, Max; Fried, Roland; Dehling, Herold
  作者单位：Ruhr University Bochum; Dortmund University of Technology
  摘要：We present a novel approach to test for heteroscedasticity of a nonstationary time series that is based on Gini's mean difference of logarithmic local sample variances. In order to analyse the large sample behaviour of our test statistic, we establish new limit theorems for U-statistics of dependent triangular arrays. We derive the asymptotic distribution of the test statistic under the null hypothesis of a constant variance and show that the test is consistent against a large class of alterna...
• ON FIXED-DOMAIN ASYMPTOTICS, PARAMETER ESTIMATION AND ISOTROPIC GAUSSIAN RANDOM FIELDS WITH MATERN COVARIANCE FUNCTIONS AI翻译
  作者：Loh, Wei-Liem; Sun, Saifei; Wen, Jun
  作者单位：National University of Singapore
  摘要：A method is proposed for estimating the microergodic parameters (including the smoothness parameter) of stationary Gaussian random fields on R-d with isotropic Matern covariance functions using irregularly spaced data. This approach uses higher-order quadratic variations and is applied to three designs, namely stratified sampling design, randomized sampling design and deformed lattice design. Microergodic parameter estimators are constructed for each of the designs. Under mild conditions, thes...
• MULTISCALE BAYESIAN SURVIVAL ANALYSIS AI翻译
  作者：Castillo, Ismael; van der Pas, Stephanie
  作者单位：Universite Paris Cite; Sorbonne Universite; Institut Universitaire de France; Vrije Universiteit Amsterdam; University of Amsterdam
  摘要：We consider Bayesian nonparametric inference in the right-censoring survival model, where modeling is made at the level of the hazard rate. We derive posterior limiting distributions for linear functionals of the hazard, and then for 'many' functionals simultaneously in appropriate multiscale spaces. As an application, we derive Bernstein-von Mises theorems for the cumulative hazard and survival functions, which lead to asymptotically efficient confidence bands for these quantities. Further, w...
• EXTREME CONDITIONAL EXPECTILE ESTIMATION IN HEAVY-TAILED HETEROSCEDASTIC REGRESSION MODELS AI翻译
  作者：Girard, Stephane; Stupfler, Gilles; Usseglio-Carleve, Antoine
  作者单位：Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Universite Grenoble Alpes (UGA); Inria; Institut National Polytechnique de Grenoble; Centre National de la Recherche Scientifique (CNRS); Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI); Universite de Rennes; Universite de Toulouse; Universite Toulouse 1 Capitole; Toulouse School of Economics
  摘要：Expectiles define a least squares analogue of quantiles. They have been the focus of a substantial quantity of research in the context of actuarial and financial risk assessment over the last decade. The behaviour and estimation of unconditional extreme expectiles using independent and identically distributed heavy-tailed observations have been investigated in a recent series of papers. We build here a general theory for the estimation of extreme conditional expectiles in heteroscedastic regre...
• STATISTICAL GUARANTEES FOR BAYESIAN UNCERTAINTY QUANTIFICATION IN NONLINEAR INVERSE PROBLEMS WITH GAUSSIAN PROCESS PRIORS AI翻译
  作者：Monard, Francois; Nickl, Richard; Paternain, Gabriel P.
  作者单位：University of California System; University of California Santa Cruz; University of Cambridge
  摘要：Bayesian inference and uncertainty quantification in a general class of nonlinear inverse regression models is considered. Analytic conditions on the regression model {g(theta) : theta is an element of Theta} and on Gaussian process priors for theta are provided such that semiparametrically efficient inference is possible for a large class of linear functionals of theta. A general Bernstein-von Mises theorem is proved that shows that the (non-Gaussian) posterior distributions are approximated ...
• MARGINAL SINGULARITY AND THE BENEFITS OF LABELS IN COVARIATE-SHIFT AI翻译
  作者：Kpotufe, Samory; Martinet, Guillaume
  作者单位：Columbia University; Princeton University
  摘要：Transfer Learning addresses common situations in Machine Leaning where little or no labeled data is available for a target prediction problem- corresponding to a distribution Q, but much labeled data is available from some related but different data distribution P. This work is concerned with the fundamental limits of transfer, that is, the limits in target performance in terms of (1) sample sizes from P and Q, and (2) differences in data distributions P, Q. In particular, we aim to address pr...
• TOTAL VARIATION REGULARIZED FRECHET REGRESSION FOR METRIC-SPACE VALUED DATA AI翻译
  作者：Lin, Zhenhua; Muller, Hans-Georg
  作者单位：National University of Singapore; University of California System; University of California Davis
  摘要：Non-Euclidean data that are indexed with a scalar predictor such as time are increasingly encountered in data applications, while statistical methodology and theory for such random objects are not well developed yet. To address the need for new methodology in this area, we develop a total variation regularization technique for nonparametric Frechet regression, which refers to a regression setting where a response residing in a metric space is paired with a scalar predictor and the target is a ...