The Annals of Statistics

最新文章

• Confidence bands in nonparametric time series regression AI翻译
  作者：Zhao, Zhibiao; Wul, Wei Biao
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Chicago
  摘要：We consider nonparametric estimation of mean regression and conditional variance (or volatility) functions in nonlinear stochastic regression models. Simultaneous confidence bands are constructed and the coverage probabilities are shown to be asymptotically correct. The imposed dependence structure allows applications in many linear and nonlinear autoregressive processes. The results are applied to the S&P 500 Index data.
• Optimal rank-based tests for homogeneity of scatter AI翻译
  作者：Hallin, Marc; Paindaveine, Davy
  作者单位：Universite Libre de Bruxelles; Universite Libre de Bruxelles
  摘要：We propose a class of locally and asymptotically optimal tests, based on multivariate ranks and signs for the homogeneity of scatter matrices in M. elliptical populations. Contrary to the existing parametric procedures, these tests remain valid without any moment assumptions, and thus are perfectly robust against heavy-tailed distributions (validity robustness). Nevertheless, they reach semiparametric efficiency bounds at correctly specified elliptical densities and maintain high powers under ...
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models AI翻译
  作者：Huang, Jian; Horowitz, Joel L.; Ma, Shuangge
  作者单位：University of Iowa; Northwestern University; Yale University
  摘要：We Study the asymptotic properties of bridge estimators in sparse, high-dimensional, linear regression models when the number of covariates may increase to infinity with the sample size. We are particularly interested in the use of bridge estimators to distinguish between covariates whose coefficients are zero and covariates whose coefficients are nonzero. We show that under appropriate conditions, bridge estimators correctly select covariates with nonzero coefficients with probability converg...
• Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models AI翻译
  作者：Chatterjee, Snigdhansu; Lahiri, Partha; Li, Huilin
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University System of Maryland; University of Maryland College Park
  摘要：Empirical best linear unbiased prediction (EBLUP) method uses a linear mixed model in combining information from different sources of information. This method is particularly useful in small area problems. The variability of an EBLUP is traditionally measured by the mean squared prediction error (MSPE), and interval estimates are generally constructed using estimates of the MSPE. Such methods have shortcomings like under-coverage or over-coverage, excessive length and lack of interpretability....
• Estimation for almost periodic processes (vol 34, pg 1115, 2006) AI翻译
  作者：Lii, Keh-Shin; Rosenblatt, Murray
  作者单位：University of California System; University of California Riverside; University of California System; University of California San Diego
• Statistical performance of support vector machines AI翻译
  作者：Blanchard, Gilles; Bousquet, Olivier; Massart, Pascal
  作者单位：Fraunhofer Gesellschaft; Fraunhofer Germany; Alphabet Inc.; Google Incorporated; Universite Paris Saclay
  摘要：The support vector machine (SVM) algorithm is well known to the computer learning community for its very good practical results. The goal of the present paper is to study this algorithm from a statistical perspective, using tools of concentration theory and empirical processes. Our main result builds on the observation made by other authors that the SVM can be viewed as a statistical regularization procedure. From this point of view, it can also be interpreted as a model selection principle us...
• Adaptive confidence bands AI翻译
  作者：Genovese, Christopher; Wasserman, Larry
  作者单位：Carnegie Mellon University
  摘要：We show that there do not exist adaptive confidence bands for curve estimation except under very restrictive assumptions. We propose instead to construct adaptive bands that cover a surrogate function f* which is close to, but simpler than, f. The surrogate captures the significant features in f. We establish lower bounds on the width for any confidence band for f* and construct a procedure that comes within a small constant factor of attaining the lower bound for finite-samples.
• Generalizing Simes' test and Hochberg's stepup procedure AI翻译
  作者：Sarkar, Sanat K.
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
  摘要：In a multiple testing problem where one is willing to tolerate a few false rejections, procedure controlling the familywise error rate (FWER) can potentially be improved in terms of its ability to detect false null hypotheses by generalizing it to control the k-FWER, the probability of falsely rejecting at least k null hypotheses, for some fixed k > 1. Simes' test for testing the intersection null hypothesis is generalized to control the k-FWER weakly, that is, under the intersection null hypo...
• Discussion: One-step sparse estimates in nonconcave penalized likelihood models AI翻译
  作者：Zhang, Cun-Hui
  作者单位：Rutgers University System; Rutgers University New Brunswick
• Rodeo: Sparse, greedy nonparametric regression AI翻译
  作者：Lafferty, John; Wasserman, Larry
  作者单位：Carnegie Mellon University; Carnegie Mellon University
  摘要：We present a greedy method for simultaneously performing local bandwidth selection and variable selection in nonparametric regression. The method starts with a local linear estimator with large bandwidths, and incrementally decreases the bandwidth of variables for which the gradient of the estimator with respect to bandwidth is large. The method-called rodeo (regularization of derivative expectation operator)-conducts a sequence of hypothesis tests to threshold derivatives, and is easy to impl...