1998

最新文章

• Breakdown theory for bootstrap quantiles AI翻译
  作者：Singh, K
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：A general formula for computing the breakdown point (in robustness) for the tth bootstrap quantile of a statistic T-n is obtained. The answer depends on t and the breakdown point of T-n. Since the bootstrap quantiles are vital ingredients of bootstrap confidence intervals, the theory has implications pertaining to robustness of bootstrap confidence intervals. For certain L and M estimators, a robustification of bootstrap is suggested via the notion of Winsorization.
• On locally uniformly linearizable high breakdown location and scale functionals AI翻译
  作者：Davies, PL
  作者单位：University of Duisburg Essen
  摘要：This article gives two constructions of a weighted mean which has a large domain, is affinely equivariant, has a locally high breakdown point and is locally uniformly linearizable. One construction is based on M-functionals with smooth defining psi- and chi-functions which are used to control the weighting. The second construction involves a locally uniformly linearizable reduction of the data to a finite set of points. This construction has the advantage of computational speed and opens up th...
• Estimating integrals of stochastic processes using space-time data AI翻译
  作者：Niu, XF
  作者单位：State University System of Florida; Florida State University
  摘要：Consider a space-time stochastic process Z(t)(x) = S(x)+ xi(t)(x) where S(x) is a signal process defined on R-d and xi(t)(x) represents measurement errors at time t. For a known measurable function v(x) on R-d and a fixed cube D subset of R-d, this paper proposes a linear estimator for the stochastic integral integral(D) v(x)S(x)dx based on space-time observations {Z(t)(x(i)): i = 1,..., n; t = 1,..., T}. Under mild conditions, the asymptotic properties of the mean squared error of the estimat...
• Information theory and superefficiency AI翻译
  作者：Barron, A; Hengartner, N
  作者单位：Yale University
  摘要：The asymptotic risk of efficient estimators with Kullback-Leibler loss in smoothly parametrized statistical models is k/2n, where k is the parameter dimension and n is the sample size. Under fairly general conditions, we given a simple information-theoretic proof that the set of parameter values where any arbitrary estimator is superefficient is negligible. The proof is based on a result of Rissanen that codes have asymptotic redundancy not smaller than (k/2)log n, except in a set of measure 0.
• Optimal repeated measurements designs: The linear optimality equations (vol 25, pg 2328, 1997) AI翻译
  作者：Kushner, HB
  作者单位：Nathan Kline Institute for Psychiatric Research
• A functional central limit theorem for regression models AI翻译
  作者：Bischoff, W
  作者单位：Helmholtz Association; Karlsruhe Institute of Technology
  摘要：Let a linear regression be given. For detecting change-points, it is usual to consider the sequence of partial sums of least squares residuals whence a partial sums process is defined. Given a sequence of exact experimental designs, we consider for each design the corresponding partial sums process. If the sequence of designs converges to a continuous design, we derive the explicit form of the limit process of the corresponding sequence of partial sums processes. This is a complicated function...
• Computation of the exact information matrix of Gaussian dynamic regression time series models AI翻译
  作者：Klein, A; Mélard, G; Zahaf, T
  作者单位：University of Amsterdam; Universite Libre de Bruxelles
  摘要：In this paper, the computation of the exact Fisher information matrix of a large class of Gaussian time series models is considered. This class, which is often called the single-input-single-output (SISO) model, includes dynamic regression with autocorrelated errors and the transfer function model, with autoregressive moving average errors. The method is based on a combination of two computational procedures: recursions for the covariance matrix of the derivatives of the state vector with resp...