2004

最新文章

• A Bernstein-von Mises theorem in the nonparametric right-censoring model AI翻译
  作者：Kim, Y; Lee, J
  作者单位：Seoul National University (SNU)
  摘要：In the recent Bayesian nonparametric literature, many examples have been reported in which Bayesian estimators and posterior distributions do not achieve the optimal convergence rate, indicating that the Bernstein-von Mises theorem does not hold. In this article, we give a positive result in this direction by showing that the Bernstein-von Mises theorem holds in survival models for a large class of prior processes neutral to the right. We also show that, for an arbitrarily given convergence ra...
• Uniform asymptotics for robust location estimates when the scale is unknown AI翻译
  作者：Salibian-Barrera, M; Zamar, RH
  作者单位：Carleton University; University of British Columbia
  摘要：Most asymptotic results for robust estimates rely on regularity conditions that are difficult to verily in practice. Moreover, these results apply to fixed distribution functions. In the robustness context the distribution of the data remains largely unspecified and hence results that hold uniformly over a set of possible distribution functions are of theoretical and practical interest. Also, it is desirable to be able to determine the size of the set of distribution functions where the unifor...
• Martingale transforms goodness-of-fit tests in regression models AI翻译
  作者：Khmaladze, EV; Koul, HL
  作者单位：Victoria University Wellington; Michigan State University
  摘要：This paper discusses two goodness-of-fit testing problems. The first problem pertains to fitting an error distribution to an assumed nonlinear parametric regression model, while the second pertains to fitting a parametric regression model when the error distribution is unknown. For the first problem the paper contains tests based on a certain martingale type transform of residual empirical processes. The advantage of this transform is that the corresponding tests are asymptotically distributio...
• Tusnady's inequality revisited AI翻译
  作者：Carter, A; Pollard, D
  作者单位：University of California System; University of California Santa Barbara; Yale University
  摘要：Tusnady's inequality is the key ingredient in the KMT/Hungarian coupling of the empirical distribution function with a Brownian bridge. We present an elementary proof of a result that sharpens the Tusnady inequality, modulo constants. Our method uses the beta integral representation of Binomial tails, simple Taylor expansion and some novel bounds for the ratios of normal tail probabilities.
• The golden chain - Discussion AI翻译
  作者：Friedman, J; Hastie, T; Rosset, S; Tibshirani, R; Zhu, J
  作者单位：Stanford University
• A Bayesian χ2 test for goodness-of-fit AI翻译
  作者：Johnson, VE
  作者单位：University of Michigan System; University of Michigan
  摘要：This article describes an extension of classical chi(2) goodness-of-fit tests to Bayesian model assessment. The extension, which essentially involves evaluating Pearson's goodness-of-fit statistic at a parameter value drawn from its posterior distribution, has the important property that it is asymptotically distributed as a chi(2) random variable on K - 1 degrees of freedom, independently of the dimension of the underlying parameter vector. By examining the posterior distribution of this stat...
• Periodic boxcar deconvolution and diophantine approximation AI翻译
  作者：Johnstone, IM; Raimondo, M
  作者单位：Stanford University; University of Sydney
  摘要：We consider the nonparametric estimation of a periodic function that is observed in additive Gaussian white noise after convolution with a boxcar, the indicator function of an interval. This is an idealized model for the problem of recovery of noisy signals and images observed with motion blur. If the length of the boxcar is rational, then certain frequencies are irretreviably lost in the periodic model. We consider the rate of convergence of estimators when the length of the boxcar is irratio...
• Statistical properties of the method of regularization with periodic Gaussian reproducing kernel AI翻译
  作者：Lin, Y; Brown, LD
  作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Pennsylvania
  摘要：The method of regularization with the Gaussian reproducing kernel is popular in the machine learning literature and successful in many practical applications. In this paper we consider the periodic version of the Gaussian kernel regularization. We show in the white noise model setting, that in function spaces of very smooth functions, such as the infinite-order Sobolev space and the space of analytic functions, the method under consideration is asymptotically minimax; in finite-order Sobolev s...
• Density estimation for biased data AI翻译
  作者：Efromovich, S
  作者单位：University of New Mexico
  摘要：The concept of biased data is well known and its practical applications range front social sciences and biology to economics and quality control. These observations arise when a sampling procedure chooses an observation with probability that depends on the value of the observation. This is an interesting sampling procedure because it favors some observations and neglects others. It is known that biasing does not change rates of nonparametric density estimation, but no results are available abo...
• The golden chain - Rejoinder AI翻译
  作者：Jiang, WX
  作者单位：Northwestern University