2004

最新文章

• Influence function and maximum bias of projection depth based estimators AI翻译
  作者：Zuo, YJ; Cui, HJ; Young, D
  作者单位：Michigan State University; Beijing Normal University; Arizona State University; Arizona State University-Tempe
  摘要：Location estimators induced from depth functions increasingly have been pursued and studied in the literature. Among them are those induced from projection depth functions. These projection depth based estimators have favorable properties among their competitors. In particular, they possess the best possible finite sample breakdown point robustness. However, robustness of estimators cannot be revealed by the finite sample breakdown point alone. The influence function, gross error sensitivity, ...
• Bayesian-motivated tests of function fit and their asymptotic frequentist properties AI翻译
  作者：Aerts, M; Claeskens, G; Hart, JD
  作者单位：Hasselt University; KU Leuven; Texas A&M University System; Texas A&M University College Station
  摘要：We propose and analyze nonparametric tests of the null hypothesis that a function belongs to a specified parametric family. The tests are based on BIC approximations, pi(BIC), to the posterior probability of the null model, and may be carried out in either Bayesian or frequentist fashion. We obtain results on the asymptotic distribution Of pi(BIC) under both the null hypothesis and local alternatives. One version Of pi(BIC), call it pi*(BIC), uses a class of models that are orthogonal to each ...
• Estimation of nonlinear models with Berkson measurement errors AI翻译
  作者：Wang, LQ
  作者单位：University of Manitoba
  摘要：This paper is concerned with general nonlinear regression models where the predictor variables are subject to Berkson-type measurement errors. The measurement errors are assumed to have a general parametric distribution, which is not necessarily normal. In addition, the distribution of the random error in the regression equation is nonparametric. A minimum distance estimator is proposed, which is based on the first two conditional moments of the response variable given the observed predictor v...
• Generalization bounds for averaged classifiers AI翻译
  作者：Freund, Y; Mansour, Y; Schapire, RE
  作者单位：Columbia University; Tel Aviv University; Princeton University
  摘要：We study a simple learning algorithm for binary classification. Instead of predicting with the best hypothesis in the hypothesis class, that is, the hypothesis that minimizes the training error, our algorithm predicts with a weighted average of all hypotheses, weighted exponentially with respect to their training error. We show that the prediction of this algorithm is much more stable than the prediction of an algorithm that predicts with the best hypothesis. By allowing the algorithm to absta...
• Statistical inference for time-inhomogeneous volatility models AI翻译
  作者：Mercurio, D; Spokoiny, V
  作者单位：Humboldt University of Berlin; Leibniz Association; Weierstrass Institute for Applied Analysis & Stochastics
  摘要：This paper offers a new approach for estimating and forecasting the volatility of financial time series. No assumption is made about the parametric form of the processes. On the contrary, we only suppose that the volatility can be approximated by a constant over some interval. In such a framework, the main problem consists of filtering this interval of time homogeneity; then the estimate of the volatility can be simply obtained by local averaging. We construct a locally adaptive volatility, es...
• Statistical behavior and consistency of classification methods based on convex risk minimization - Discussion AI翻译
  作者：Bartlett, PL; Jordan, MI; McAuliffe, JD
  作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley
• Approximating a sequence of observations by a simple process AI翻译
  作者：Rosenberg, D; Solan, E; Vieille, N
  作者单位：Universite Paris 13; Northwestern University; Tel Aviv University
  摘要：Given an arbitrary long but finite sequence of observations from a finite set, we construct a simple process that approximates the sequence, in the sense that with high probability the empirical frequency, as well as the empirical one-step transitions along a realization from the approximating process, are close to that of the given sequence. We generalize the result to the case where the one-step transitions are required to be in given polyhedra.
• Nonparametric estimation of scalar diffusions based on low frequency data AI翻译
  作者：Gobet, E; Hoffmann, M; Reiss, M
  作者单位：Institut Polytechnique de Paris; Ecole Polytechnique; ENSTA Paris; Universite Paris Cite; Sorbonne Universite; Humboldt University of Berlin
  摘要：We study the problem of estimating the coefficients of a diffusion (X-t, t greater than or equal to 0); the estimation is based on discrete data X-nDelta, n = 0, 1,..., N. The sampling frequency Delta(-1) is constant, and asymptotics are taken as the number N of observations tends to infinity. We prove that the problem of estimating both the diffusion coefficient (the volatility) and the drift in a nonparametric setting is ill-posed: the minimax rates of convergence for Sobolev constraints and...
• The Hough transform estimator AI翻译
  作者：Goldenshluger, A; Zeevi, A
  作者单位：University of Haifa; Columbia University
  摘要：This article pursues a statistical study of the Hough transform, the celebrated computer vision algorithm used to detect the presence of lines in a noisy image. We first study asymptotic properties of the Hough transform estimator, whose objective is to find the line that best fits a set of planar points. In particular, we establish strong consistency and rates of convergence, and characterize the limiting distribution of the Hough transform estimator. While the convergence rates are seen to b...
• Least angle regression - Discussion AI翻译
  作者：Rosset, S; Zhu, J
  作者单位：International Business Machines (IBM); IBM USA; University of Michigan System; University of Michigan