2008

最新文章

• PROFILE-KERNEL LIKELIHOOD INFERENCE WITH DIVERGING NUMBER OF PARAMETERS AI翻译
  作者：Lam, Clifford; Fan, Jianqing
  作者单位：Princeton University
  摘要：The generalized varying coefficient partially linear model with a growing number of predictors arises in many contemporary scientific endeavor. In this paper we set foot on both theoretical and practical sides of profile likelihood estimation and inference. When the number of parameters grows with sample size, the existence and asymptotic normality of the profile likelihood estimator are established under some regularity conditions. Profile likelihood ratio inference for the growing number of ...
• A multivariate central limit theorem for randomized orthogonal array sampling designs in computer experiments AI翻译
  作者：Loh, Wei-Liem
  作者单位：National University of Singapore
  摘要：Let f : [0, 1)(d) -> R be an integrable function. An objective of many computer experiments is to estimate integral(d)([0, 1)) f (x) dx by evaluating f at a finite number of points in [0, 1)(d). There is a design issue in the choice of these points and a popular choice is via the use of randomized orthogonal arrays. This article proves a multivariate central limit theorem for a class of randomized orthogonal array sampling designs [Owen Statist. Sinica 2 (1992a) 439-452] as well as for a class...
• The sparsity and bias of the lasso selection in high-dimensional linear regression AI翻译
  作者：Zhang, Cun-Hui; Huang, Jian
  作者单位：Rutgers University System; Rutgers University New Brunswick; University of Iowa
  摘要：Meinshausen and Buhlmann [Ann. Statist. 34 (2006) 1436-1462] showed that, for neighborhood selection in Gaussian graphical models, under a neighborhood stability condition, the LASSO is consistent, even when the number of variables is of greater order than the sample size. Zhao and Yu [(2006) J. Machine Learning Research 7 2541-2567] formalized the neighborhood stability condition in the context of linear regression as a strong irrepresentable condition. That paper showed that under this condi...
• Statistical modeling of causal effects in continuous time AI翻译
  作者：Lok, Judith J.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health
  摘要：This article studies the estimation of the causal effect of a time-varying treatment on time-to-an-event or on some other continuously distributed outcome. The paper applies to the situation where treatment is repeatedly adapted to time-dependent patient characteristics. The treatment effect cannot be estimated by simply conditioning on these time-dependent patient characteristics, as they may themselves be indications of the treatment effect. This time-dependent confounding is common in obser...
• EVALUATION OF FORMAL POSTERIOR DISTRIBUTIONS VIA MARKOV CHAIN ARGUMENTS AI翻译
  作者：Eaton, Morris L.; Hobert, James P.; Jones, Galin L.; Lai, Wen-Lin
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; State University System of Florida; University of Florida; Providence University - Taiwan
  摘要：We consider evaluation of proper posterior distributions obtained from improper prior distributions. Our context is estimating a bounded function phi of a parameter when the loss is quadratic. If the posterior mean of 0 is admissible for all bounded phi, the posterior is strongly admissible. We give sufficient conditions for strong admissibility. These conditions involve the recurrence of a Markov chain associated with the estimation problem. We develop general sufficient conditions for recurr...
• STEIN ESTIMATION FOR THE DRIFT OF GAUSSIAN PROCESSES USING THE MALLIAVIN CALCULUS AI翻译
  作者：Privault, Nicolas; Reveillac, Anthony
  作者单位：City University of Hong Kong; La Rochelle Universite
  摘要：We consider the nonparametric functional estimation of the drift of a Gaussian process via minimax and Bayes estimators. In this context, we construct superefficient estimators of Stein type for such drifts using the Malliavin integration by parts formula and superharmonic functionals on Gaussian space. Our results are illustrated by numerical simulations and extend the construction of James-Stein type estimators for Gaussian processes by Berger and Wolpert [J. Multivariate Anal. 13 (1983) 401...
• Rates of contraction of posterior distributions based on Gaussian process priors AI翻译
  作者：Van Der Wart, A. W.; Van Zanten, J. H.
  作者单位：Vrije Universiteit Amsterdam
  摘要：We derive rates of contraction of posterior distributions on nonparametric or semiparametric models based on Gaussian processes. The rate of contraction is shown to depend on the position of the true parameter relative to the reproducing kernel Hilbert space of the Gaussian process and the small ball probabilities of the Gaussian process. We determine these quantities for a range of examples of Gaussian priors and in several statistical settings. For instance, we consider the rate of contracti...
• LEARNING BY MIRROR AVERAGING AI翻译
  作者：Juditsky, A.; Rigollet, P.; Tsybakov, A. B.
  作者单位：Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Inria; University System of Georgia; Georgia Institute of Technology; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Sorbonne Universite; Universite Paris Cite; Institut Polytechnique de Paris; ENSAE Paris
  摘要：Given a finite collection of estimators or classifiers, we study the problem of model selection type aggregation, that is, we construct a new estimator or classifier, called aggregate, which is nearly as good as the best among them with respect to a given risk criterion. We define our aggregate by a simple recursive procedure which solves an auxiliary stochastic linear programming problem related to the original nonlinear one and constitutes a special case of the mirror averaging algorithm. We...
• The distribution of maxima of approximately Gaussian random fields AI翻译
  作者：Nardi, Yuval; Siegmund, David O.; Yakir, Benjamin
  作者单位：Carnegie Mellon University; Stanford University; Hebrew University of Jerusalem
  摘要：Motivated by the problem of testing for the existence of a signal of known parametric structure and unknown location (as explained below) against a noisy background, we obtain for the maximum of a centered, smooth random field an approximation for the tail of the distribution. For the motivating class of problems this gives approximately the significance level of the maximum score test. The method is based on an application of a likelihood-ratio-identity followed by approximations of local fie...
• Variable selection in semiparametric regression modeling AI翻译
  作者：Li, Runze; Liang, Hua
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Rochester
  摘要：In this paper, we are concerned with how to select significant variables in semiparametric modeling. Variable selection for semiparametric regression models consists of two components: model selection for nonparametric components and selection of significant variables for the parametric portion. Thus, semiparametric variable selection is much more challenging than parametric variable selection (e.g., linear and generalized linear models) because traditional variable selection procedures includ...