4期

最新文章

• THE SPARSE LAPLACIAN SHRINKAGE ESTIMATOR FOR HIGH-DIMENSIONAL REGRESSION AI翻译
  作者：Huang, Jian; Ma, Shuangge; Li, Hongzhe; Zhang, Cun-Hui
  作者单位：University of Iowa; Yale University; University of Pennsylvania; Rutgers University System; Rutgers University New Brunswick
  摘要：We propose a new penalized method for variable selection and estimation that explicitly incorporates the correlation patterns among predictors. This method is based on a combination of the minimax concave penalty and Laplacian quadratic associated with a graph as the penalty function. We call it the sparse Laplacian shrinkage (SLS) method. The SLS uses the minimax concave penalty for encouraging sparsity and Laplacian quadratic penalty for promoting smoothness among coefficients associated wit...
• ON THE RANGE OF VALIDITY OF THE AUTOREGRESSIVE SIEVE BOOTSTRAP AI翻译
  作者：Kreiss, Jens-Peter; Paparoditis, Efstathios; Politis, Dimitris N.
  作者单位：Braunschweig University of Technology; University of Cyprus; University of California System; University of California San Diego
  摘要：We explore the limits of the autoregressive (AR) sieve bootstrap, and show that its applicability extends well beyond the realm of linear time series as has been previously thought. In particular, for appropriate statistics, the AR-sieve bootstrap is valid for stationary processes possessing a general Wold-type autoregressive representation with respect to a white noise; in essence, this includes all stationary, purely nondeterministic processes, whose spectral density is everywhere positive. ...
• STOCHASTIC EXPANSIONS USING CONTINUOUS DICTIONARIES: LEVY ADAPTIVE REGRESSION KERNELS AI翻译
  作者：Wolpert, Robert L.; Clyde, Merlise A.; Tu, Chong
  作者单位：Duke University; Pacific Investment Management Company, LLC
  摘要：This article describes a new class of prior distributions for nonparametric function estimation. The unknown function is modeled as a limit of weighted sums of kernels or generator functions indexed by continuous parameters that control local and global features such as their translation, dilation, modulation and shape. Levy random fields and their stochastic integrals are employed to induce prior distributions for the unknown functions or, equivalently, for the number of kernels and for the p...
• GLOBAL SELF-WEIGHTED AND LOCAL QUASI-MAXIMUM EXPONENTIAL LIKELIHOOD ESTIMATORS FOR ARMA-GARCH/IGARCH MODELS AI翻译
  作者：Zhu, Ke; Ling, Shiqing
  作者单位：Hong Kong University of Science & Technology
  摘要：This paper investigates the asymptotic theory of the quasi-maximum exponential likelihood estimators (QMELE) for ARMA-GARCH models. Under only a fractional moment condition, the strong consistency and the asymptotic normality of the global self-weighted QMELE are obtained. Based on this self-weighted QMELE, the local QMELE is showed to be asymptotically normal for the ARMA model with GARCH (finite variance) and IGARCH errors. A formal comparison of two estimators is given for some cases. A sim...
• ORACLE INEQUALITIES AND OPTIMAL INFERENCE UNDER GROUP SPARSITY AI翻译
  作者：Lounici, Karim; Pontil, Massimiliano; van de Geer, Sara; Tsybakov, Alexandre B.
  作者单位：University System of Georgia; Georgia Institute of Technology; University of London; University College London; Swiss Federal Institutes of Technology Domain; ETH Zurich; Institut Polytechnique de Paris; ENSAE Paris
  摘要：We consider the problem of estimating a sparse linear regression vector beta* under a Gaussian noise model, for the purpose of both prediction and model selection. We assume that prior knowledge is available on the sparsity pattern, namely the set of variables is partitioned into prescribed groups, only few of which are relevant in the estimation process. This group sparsity assumption suggests us to consider the Group Lasso method as a means to estimate beta*. We establish oracle inequalities...
• ESTIMATION AND VARIABLE SELECTION FOR GENERALIZED ADDITIVE PARTIAL LINEAR MODELS AI翻译
  作者：Wang, Li; Liu, Xiang; Liang, Hua; Carroll, Raymond J.
  作者单位：University System of Georgia; University of Georgia; University of Rochester; Texas A&M University System; Texas A&M University College Station
  摘要：We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection proced...
• ON BAYES' THEOREM FOR IMPROPER MIXTURES AI翻译
  作者：McCullagh, Peter; Han, Han
  作者单位：University of Chicago
  摘要：Although Bayes's theorem demands a prior that is a probability distribution on the parameter space, the calculus associated with Bayes's theorem sometimes generates sensible procedures from improper priors, Pitman's estimator being a good example. However, improper priors may also lead to Bayes procedures that are paradoxical or otherwise unsatisfactory, prompting some authors to insist that all priors be proper. This paper begins with the observation that an improper measure on 8 satisfying K...
• OPTIMAL MODEL SELECTION FOR DENSITY ESTIMATION OF STATIONARY DATA UNDER VARIOUS MIXING CONDITIONS AI翻译
  作者：Lerasle, Matthieu
  作者单位：Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite de Toulouse; Institut National des Sciences Appliquees de Toulouse; Universite Toulouse III - Paul Sabatier; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
  摘要：We propose a block-resampling penalization method for marginal density estimation with nonnecessary independent observations. When the data are beta or tau-mixing, the selected estimator satisfies oracle inequalities with leading constant asymptotically equal to 1. We also prove in this setting the slope heuristic, which is a data-driven method to optimize the leading constant in the penalty.
• STATISTICAL INFERENCE FOR TIME-CHANGED LEVY PROCESSES VIA COMPOSITE CHARACTERISTIC FUNCTION ESTIMATION AI翻译
  作者：Belomestny, Denis
  作者单位：University of Duisburg Essen
  摘要：In this article, the problem of semi-parametric inference on the parameters of a multidimensional Levy process L-t with independent components based on the low-frequency observations of the corresponding time-changed Levy process L-T(,), where T is a nonnegative, nondecreasing real-valued process independent of L-t, is studied. We show that this problem is closely related to the problem of composite function estimation that has recently gotten much attention in statistical literature. Under su...
• NEW ESTIMATORS OF THE PICKANDS DEPENDENCE FUNCTION AND A TEST FOR EXTREME-VALUE DEPENDENCE AI翻译
  作者：Buecher, Axel; Dette, Holger; Volgushev, Stanislav
  作者单位：Ruhr University Bochum
  摘要：We propose a new class of estimators for Pickands dependence function which is based on the concept of minimum distance estimation. An explicit integral representation of the function A* (t), which minimizes a weighted L(2)-distance between the logarithm of the copula C(y(1-t), y(t)) and functions of the form A (t) log(y) is derived. If the unknown copula is an extreme-value copula, the function A* (t) coincides with Pickands dependence function. Moreover, even if this is not the case, the fun...