4期

最新文章

• FAST LEARNING RATES IN STATISTICAL INFERENCE THROUGH AGGREGATION AI翻译
  作者：Audibert, Jean-Yves
  作者单位：Universite Gustave-Eiffel; Institut Polytechnique de Paris; Ecole Nationale des Ponts et Chaussees; Universite PSL; Ecole Normale Superieure (ENS); Centre National de la Recherche Scientifique (CNRS); Inria
  摘要：We develop minimax optimal risk bounds for the general learning task consisting in predicting as well as the best function in a reference set g up to the smallest possible additive term, called the convergence rate. When the reference set is finite and when n denotes the size of the training data, we provide minimax convergence rates of the form C(log|g|/n)(nu) with tight evaluation of the positive constant C and with exact 0 < nu <= 1, the latter value depending on the convexity of the loss f...
• OPTIMAL DISCRIMINATION DESIGNS AI翻译
  作者：Dette, Holger; Titoff, Stefanie
  作者单位：Ruhr University Bochum
  摘要：We consider the problem of constructing optimal designs for model discrimination between competing regression models. Various new properties of optimal designs with respect to the popular T-optimality criterion are derived, which in many circumstances allow an explicit determination of T-optimal designs. It is also demonstrated, that in nested linear models the number of support points of T-optimal designs is usually too small to estimate all parameters in the extended model. In many cases T-o...
• MAXIMUM LIKELIHOOD ESTIMATION FOR α-STABLE AUTOREGRESSIVE PROCESSES AI翻译
  作者：Andrews, Beth; Calder, Matthew; Davis, Richard A.
  作者单位：Northwestern University; Columbia University
  摘要：We consider maximum likelihood estimation for both causal and noncausal autoregressive time series processes with non-Gaussian alpha-stable noise. A nondegenerate limiting distribution is given for maximum likelihood estimators of the parameters of the autoregressive model equation and the parameters of the stable noise distribution. The estimators for the autoregressive parameters are n(1/alpha)-consistent and converge in distribution to the maximizer of a random function. The form of this li...
• KERNEL DIMENSION REDUCTION IN REGRESSION AI翻译
  作者：Fukumizu, Kenji; Bach, Francis R.; Jordan, Michael I.
  作者单位：Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan; Inria; Universite PSL; Ecole Normale Superieure (ENS); Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Information Sciences & Technologies (INS2I); University of California System; University of California Berkeley
  摘要：We present a new methodology for sufficient dimension reduction (SDR). Our methodology derives directly from the formulation of SDR in terms of the conditional independence of the covariate X from the response Y, given the projection of X on the central subspace [cf. J. Amer Statist. Assoc. 86 (1991) 316-342 and Regression Graphics (1998) Wiley]. We show that this conditional independence assertion can be characterized in terms of conditional covariance operators on reproducing kernel Hilbert ...
• COVARIATE-ADJUSTED NONLINEAR REGRESSION AI翻译
  作者：Cui, Xia; Guo, Wensheng; Lin, Lu; Zhu, Lixing
  作者单位：Shandong University; Hong Kong Baptist University; University of Pennsylvania; East China Normal University
  摘要：In this paper, we propose a covariate-adjusted nonlinear regression model. In this model, both the response and predictors can only be observed after being distorted by some multiplicative factors. Because of nonlinearity, existing methods for the linear setting cannot be directly employed. To attack this problem, we propose estimating the distorting functions by nonparametrically regressing the predictors and response on the distorting covariate; then, nonlinear least squares estimators for t...
• NONPARAMETRIC EMPIRICAL BAYES AND COMPOUND DECISION APPROACHES TO ESTIMATION OF A HIGH-DIMENSIONAL VECTOR OF NORMAL MEANS AI翻译
  作者：Brown, Lawrence D.; Greenshtein, Eitan
  作者单位：University of Pennsylvania; Duke University
  摘要：We consider the classical problem of estimating a vector mu = (mu(1,) ..., mu(n)) based on independent observations Yi similar to N(mu(i), 1), i = 1, ..., n. Suppose mu(i), i = 1, ..., n are independent realizations from a completely unknown G. We suggest an easily computed estimator (mu) over cap, such that the ratio of its risk E((mu) over cap - mu)(2) with that of the Bayes procedure approaches 1. A related compound decision result is also obtained. Our asymptotics is of a triangular array;...
• ON THE COMPUTATIONAL COMPLEXITY OF MCMC-BASED ESTIMATORS IN LARGE SAMPLES AI翻译
  作者：Belloni, Alexandre; Chernozhukov, Victor
  作者单位：Duke University; Massachusetts Institute of Technology (MIT)
  摘要：In this paper we examine the implications of the statistical large sample theory for the computational complexity of Bayesian and quasi-Bayesian estimation carried out using Metropolis random walks. Our analysis is motivated by the Laplace-Bernstein-Von Mises central limit theorem, which states that in large samples the posterior or quasi-posterior approaches a. normal density. Using the conditions required for the central limit theorem to hold, we establish polynomial bounds on the computatio...
• GENERAL MAXIMUM LIKELIHOOD EMPIRICAL BAYES ESTIMATION OF NORMAL MEANS AI翻译
  作者：Jiang, Wenhua; Zhang, Cun-Hui
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：We propose a general maximum likelihood empirical Bayes (GMLEB) method for the estimation of a mean vector based on observations with i.i.d. normal errors. We prove that under mild moment conditions on the unknown means, the average mean squared error (MSE) of the GMLEB is within an infinitesimal fraction of the minimum average MSE among all separable estimators which use a single deterministic estimating function on individual observations, provided that the risk is of greater order than (log...
• ON THE ADAPTIVE ELASTIC-NET WITH A DIVERGING NUMBER OF PARAMETERS AI翻译
  作者：Zou, Hui; Zhang, Hao Helen
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; North Carolina State University
  摘要：We consider the problem of model selection and estimation in situations where the number of parameters diverges with the sample size. When the dimension is high, an ideal method should have the oracle property [J Amer. Statist. Assoc. 96 (2001) 1348-1360] and [Ann. Statist. 32 (2004) 928-961] which ensures the optimal large sample performance. Furthermore, the high-dimensionality often induces the collinearity problem, which should be properly handled by the ideal method. Many existing variabl...
• ASYMPTOTICS FOR POSTERIOR HAZARDS AI翻译
  作者：De Blasi, Pierpaolo; Peccati, Giovanni; Prunster, Igor
  作者单位：University of Turin; Universite Paris Nanterre; Universite Paris Saclay
  摘要：An important issue in survival analysis is the investigation and the modeling of hazard rates. Within a Bayesian nonparametric framework, a natural and popular approach is to model hazard rates as kernel mixtures with respect to a completely random measure. In this paper we provide a comprehensive analysis of the asymptotic behavior of such models. We investigate consistency of the posterior distribution and derive fixed sample size central limit theorems for both linear and quadratic function...