1998

最新文章

• Nonlinear principal components I. Absolutely continuous random variables with positive bounded densities AI翻译
  作者：Salinelli, E
  作者单位：University of Turin
  摘要：Nonlinear principal components for an absolutely continuous random vector X with positive bounded density are defined as the solution of a variational problem in a suitable function space. In this way transformations depending on all the components of X are obtained. Some properties of nonlinear principal components are proved: in particular, it is shown that the set of nonlinear principal transformations of X is an orthonormal basis for the function space associated with the optimal problem. ...
• Wavelet shrinkage for nonequispaced samples AI翻译
  作者：Cai, TT; Brown, LD
  作者单位：Purdue University System; Purdue University; University of Pennsylvania
  摘要：Standard wavelet shrinkage procedures for nonparametric regression are restricted to equispaced samples. There, data are transformed into empirical wavelet coefficients and threshold rules are applied to the coefficients. The estimators are obtained via the inverse transform of the denoised wavelet coefficients. In many applications, however, the samples are nonequispaced. It can be shown that these procedures would produce suboptimal estimators if they were applied directly to nonequispaced s...
• Arcing classifiers - Discussion AI翻译
  作者：Freund, Y; Schapire, RE
  作者单位：AT&T
• MCMC convergence diagnosis via multivariate bounds on log-concave densities AI翻译
  作者：Brooks, SP
  作者单位：University of Bristol
  摘要：We begin by showing how piecewise linear bounds may be devised, which bound both above and below any concave log-density in general dimensions. We then show how these bounds may be used to gain an upper bound to the volume in the tails outside the convex hull of the sample path in order to assess how well the sampler has explored the target distribution. This method can be used as a stand-alone diagnostic to determine when the sampler output provides a reliable basis for inference on the stati...
• Optimal designs for the identification of the order of a Fourier regression AI翻译
  作者：Dette, H; Haller, G
  作者单位：Ruhr University Bochum
  摘要：For the Fourier regression model, we determine optimal designs for identifying the order of periodicity. It is shown that the optimal design problem for trigonometric regression models is equivalent to the problem of optimal design for discriminating between certain homo- and heteroscedastic polynomial regression models. These optimization problems are then solved using the theory of canonical moments, and the optimal discriminating designs for the Fourier regression model can be found explici...
• Regression-type inference in nonparametric autoregression AI翻译
  作者：Neumann, MH; Kreiss, JP
  作者单位：Humboldt University of Berlin; Braunschweig University of Technology
  摘要：We derive a strong approximation of a local polynomial estimator (LPE) in nonparametric autoregression by an LPE in a corresponding nonparametric regression model. This generally suggests the application of regression-typical tools for statistical inference in nonparametric autoregressive models. It provides an important simplification for the bootstrap method to be used: It is enough to mimic the structure of a nonparametric regression model rather than to imitate the more complicated process...
• Simultaneous sharp estimation of functions and their derivatives AI翻译
  作者：Efromovich, S
  作者单位：University of New Mexico
  摘要：A data-driven estimate is given that, over a Sobolev space, is simultaneously asymptotically sharp minimax for estimating both the function and its derivatives under integrated squared error loss. It is also shown that linear estimates cannot be simultaneously asymptotically sharp minimax over a given Sobolev space.
• Quickest detection with exponential penalty for delay AI翻译
  作者：Poor, HV
  作者单位：Princeton University
  摘要：The problem of detecting a change in the probability distribution of a random sequence is considered. Stopping times are derived that optimize the tradeoff between detection delay and false alarms within two criteria. In both cases, the detection delay is penalized exponentially rather than Linearly, as has been the case in previous formulations of this problem. The first of these two criteria is to minimize a worst-case measure of the exponential detection delay within a lower-bound constrain...
• Linear regression with interval censored data AI翻译
  作者：Li, G; Zhang, CH
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：This paper concerns linear regression with interval censored data. M-estimators for the regression coefficients are derived. Asymptotic consistency and normality of the M-estimators are obtained via an exponential inequality for U-statistics. Asymptotically efficient estimators are provided under mild conditions.
• Quadratic and inverse regressions for Wishart distributions AI翻译
  作者：Letac, G; Massam, H
  作者单位：Universite de Toulouse; Universite Toulouse III - Paul Sabatier; York University - Canada
  摘要：If U and V are independent random variables which are gamma distributed with the same scale parameter, then there exist a and b in R such that E(U | U + V) = a(U + V) and E(U-2 | U + V) = b(U + V)(2). This, in fact, is characteristic of gamma distributions. Our paper extends this property to the Wishart distributions in a suitable way, by replacing the real number U-2 by a pair of quadratic functions of the symmetric matrix U. This leads to a new characterization of the Wishart distributions, ...