5期

最新文章

• Goodness-of-fit tests via phi-divergences AI翻译
  作者：Jager, Leah; Wellner, Jon A.
  作者单位：Grinnell College; University of Washington; University of Washington Seattle
  摘要：A unified family of goodness-of-fit tests based on phi-divergences is introduced and studied. The new family of test statistics S-n(s) includes both the supremum version of the Anderson-Darling statistic and the test statistic of Berk and Jones [Z. Wahrsch. Verw. Gebiete 47 (1979) 47-59] as special cases (s = 2 and s = 1, resp.). We also introduce integral versions of the new statistics. We show that the asymptotic null distribution theory of Berk and Jones [Z. Wahrsch. Verw. Gebiete 47 (1979)...
• Estimation of the Hurst parameter from discrete noisy data AI翻译
  作者：Gloter, Arnaud; Hoffmann, Marc
  作者单位：Universite Gustave-Eiffel; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Paris-Est-Creteil-Val-de-Marne (UPEC)
  摘要：We estimate the Hurst parameter H of a fractional Brownian motion from discrete noisy data observed along a high frequency sampling scheme. The presence of systematic experimental noise makes recovery of H more difficult since relevant information is mostly contained in the high frequencies of the signal. We quantify the difficulty of the statistical problem in a min-max sense: we prove that the rate n(-1/(4H+2)) is optimal for estimating H and propose rate optimal estimators based on adaptive...
• On surrogate dimension reduction for measurement error regression: An invariance law AI翻译
  作者：Li, Bing; Yin, Xiangrong
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University System of Georgia; University of Georgia
  摘要：We consider a general nonlinear regression problem where the predictors contain measurement error. It has been recently discovered that several well-known dimension reduction methods, such as OLS, SIR and pHd, can be performed on the surrogate regression problem to produce consistent estimates for the original regression problem involving the unobserved true predictor. In this paper we establish a general invariance law between the surrogate and the original dimension reduction spaces, which i...
• On the degrees of freedom of the lasso AI翻译
  作者：Zou, Hui; Hastie, Trevor; Tibshirani, Robert
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Stanford University
  摘要：We study the effective degrees of freedom of the lasso in the framework of Stein's unbiased risk estimation (SURE). We show that the number of nonzero coefficients is an unbiased estimate for the degrees of freedom of the lasso-a conclusion that requires no special assumption on the predictors. In addition, the unbiased estimator is shown to be asymptotically consistent. With these results on hand, various model selection criteria-C-p, AIC and BIC-are available, which, along with the LARS algo...
• Computer model validation with functional output AI翻译
  作者：Bayarri, M. J.; Berger, J. O.; Cafeo, J.; Garcia-Donato, G.; Liu, F.; Palomo, J.; Parthasarathy, R. J.; Paulo, R.; Sacks, J.; Walsh, D.
  作者单位：University of Valencia; Duke University; General Motors; Universidad de Castilla-La Mancha; Universidad Rey Juan Carlos; Universidade de Lisboa; Massey University
  摘要：A key question in evaluation of computer models is Does the computer model adequately represent reality? A six-step process for computer model validation is set out in Bayarri et al. [Technometrics 49 (2007) 138-154] (and briefly summarized below), based on comparison of computer model runs with field data of the process being modeled. The methodology is particularly suited to treating the major issues associated with the validation process: quantifying multiple sources of error and uncertaint...
• Spatial aggregation of local likelihood estimates with applications to classification AI翻译
  作者：Belomestny, Denis; Spokoiny, Vladimir
  作者单位：Leibniz Association; Weierstrass Institute for Applied Analysis & Stochastics
  摘要：This paper presents a new method for spatially adaptive local (constant) likelihood estimation which applies to a broad class of nonparametric models, including the Gaussian, Poisson and binary response models. The main idea of the method is, given a sequence of local likelihood estimates (weak estimates), to construct a new aggregated estimate whose pointwise risk is of order of the smallest risk among all weak estimates. We also propose a new approach toward selecting the parameters of the p...
• Iterative estimating equations: Linear convergence and asymptotic properties AI翻译
  作者：Jiang, Jiming; Luan, Yihui; Wang, You-Gan
  作者单位：University of California System; University of California Davis; Shandong University; Commonwealth Scientific & Industrial Research Organisation (CSIRO)
  摘要：We propose an iterative estimating equations procedure for analysis of longitudinal data. We show that, under very mild conditions, the probability that the procedure converges at an exponential rate tends to one as the sample size increases to infinity. Furthermore, we show that the limiting estimator is consistent and asymptotically efficient, as expected. The method applies to semiparametric regression models with unspecified covariances among the observations. In the special case of linear...
• Asymptotic theory of least squares estimators for nearly unstable processes under strong dependence AI翻译
  作者：Buchmann, Borls; Chan, Ngai Hang
  作者单位：Australian National University; Chinese University of Hong Kong
  摘要：This paper considers the effect of least squares procedures for nearly unstable linear time series with strongly dependent innovations. Under a general framework and appropriate scaling, it is shown that ordinary least squares procedures converge to functionals of fractional Ornstein-Uhlenbeck processes. We use fractional integrated noise as an example to illustrate the important ideas. In this case, the functionals bear only formal analogy to those in the classical framework with uncorrelated...