2008

最新文章

• Stability of the Gibbs sampler for Bayesian hierarchical models AI翻译
  作者：Papaspiliopoulos, Omiros; Roberts, Gareth
  作者单位：University of Warwick
  摘要：We characterize the convergence of the Gibbs sampler which samples from the joint posterior distribution of parameters and missing data in hierarchical linear models with arbitrary symmetric error distributions. We show that the convergence can be uniform, geometric or subgeometric depending on the relative tail behavior of the error distributions, and on the parametrization chosen. Our theory is applied to characterize the convergence of the Gibbs sampler on latent Gaussian process models. We...
• HIGH-DIMENSIONAL CLASSIFICATION USING FEATURES ANNEALED INDEPENDENCE RULES AI翻译
  作者：Fan, Jianqing; Fan, Yingying
  作者单位：Princeton University; University of Southern California; Harvard University
  摘要：Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is poorly understood. In a seminal paper, Bickel and Levina [Bernoulli 10 (2004) 989-1010] show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even...
• MULTIVARIATE ANALYSIS AND JACOBI ENSEMBLES: LARGEST EIGENVALUE, TRACY-WIDOM LIMITS AND RATES OF CONVERGENCE AI翻译
  作者：Johnstone, Iain M.
  作者单位：Stanford University
  摘要：Let A and B be independent, central Wishart matrices in p variables with common covariance and having in and n degrees of freedom, respectively. The distribution of the largest eigenvalue of (A + B)(-1) B has numerous applications in multivariate statistics, but is difficult to calculate exactly. Suppose that in and n grow in proportion to p. We show that after centering and scaling, the distribution is approximated to second-order, O(p(-2/3)), by the Tracy-Widom law. The results are obtained ...
• TILTED EULER CHARACTERISTIC DENSITIES FOR CENTRAL LIMIT RANDOM FIELDS, WITH APPLICATION TO BUBBLES AI翻译
  作者：Chamandy, N.; Worsley, K. J.; Taylor, J.; Gosselin, F.
  作者单位：McGill University; Alphabet Inc.; Google Incorporated; University of Chicago; Stanford University; Universite de Montreal
  摘要：Local increases in the mean of a random field are detected (conservatively) by thresholding a field of test statistics at a level u chosen to control the tail probability or p-value of its maximum. This p-value is approximated by the expected Euler characteristic (EC) of the excursion set of the test statistic field above u, denoted E phi (A(u)). Under isotropy, one can use the expansion E phi(A(u)) = Sigma(k) V(k rho k()u), where V-k is an intrinsic volume of the parameter space and rho(k) is...
• Higher order semiparametric frequentist inference with the profile sampler AI翻译
  作者：Cheng, Guang; Kosorok, Michael R.
  作者单位：Duke University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：We consider higher order frequentist inference for the parametric component of a semiparametric model based on sampling from the posterior profile distribution. The first order validity of this procedure established by Lee, Kosorok and Fine in [J. American Statist. Assoc. 100 (2005) 960969] is extended to second-order validity in the setting where the infinite-dimensional nuisance parameter achieves the parametric rate. Specifically, we obtain higher order estimates of the maximum profile like...
• Statistics of extremes by oracle estimation AI翻译
  作者：Grama, Ion; Spokoiny, Vladimir
  作者单位：Leibniz Association; Weierstrass Institute for Applied Analysis & Stochastics
  摘要：We use the fitted Pareto law to construct an accompanying approximation of the excess distribution function. A selection rule of the location of the excess distribution function is proposed based on a stagewise lack-of-fit testing procedure. Our main result is an oracle type inequality for the Kullback-Leibler loss.
• Closed-form likelihood expansions for multivariate diffusions AI翻译
  作者：Ait-Sahalia, Yacine
  作者单位：Princeton University; National Bureau of Economic Research
  摘要：This paper provides closed-form expansions for the log-likelihood function of multivariate diffusions sampled at discrete time intervals. The coefficients of the expansion are calculated explicitly by exploiting the special structure afforded by the diffusion model. Examples of interest in financial statistics and Monte Carlo evidence are included, along with the convergence of the expansion to the true likelihood function.
• OPERATOR NORM CONSISTENT ESTIMATION OF LARGE-DIMENSIONAL SPARSE COVARIANCE MATRICES AI翻译
  作者：El Karoui, Noureddine
  作者单位：University of California System; University of California Berkeley
  摘要：Estimating covariance matrices is a problem of fundamental importance in multivariate statistics. In practice it is increasingly frequent to work with data matrices X of dimension if x p, where p and n are both large. Results from random matrix theory show very clearly that in this setting, standard estimators like the sample covariance matrix perform in general very poorly. In this large n, large p setting, it is sometimes the case that practitioners are willing to assume that many elements o...
• Normalized least-squares estimation in time-varying ARCH models AI翻译
  作者：Fryzlewicz, Piotr; Sapatinas, Theofanis; Rao, Suhasini Subba
  作者单位：University of Bristol; University of Cyprus; Texas A&M University System; Texas A&M University College Station; Ruprecht Karls University Heidelberg
  摘要：We investigate the time-varying ARCH (tvARCH) process. It is shown that it can be used to describe the slow decay of the sample autocorrelations of the squared returns often observed in financial time series, which warrants the further study of parameter estimation methods for the model. Since the parameters are changing over time, a successful estimator needs to perform well for small samples. We propose a kernel normalized-least-squares (kernel-NLS) estimator which has a closed form, and thu...
• Quadratic distances on probabilities: A unified foundation AI翻译
  作者：Lindsay, Bruce G.; Markatou, Marianthi; Ray, Surajit; Yang, Ke; Chen, Shu-Chuan
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Columbia University; Boston University; Cytokinetics, Inc.; Arizona State University; Arizona State University-Tempe; National Cheng Kung University
  摘要：This work builds a unified framework for the study of quadratic form distance measures as they are used in assessing the goodness of fit of models. Many important procedures have this structure, but the theory for these methods is dispersed and incomplete. Central to the statistical analysis of these distances is the spectral decomposition of the kernel that generates the distance. We show how this determines the limiting distribution of natural goodness-of-fit tests. Additionally, we develop ...