1996

最新文章

• Function estimation via wavelet shrinkage for long-memory data AI翻译
  作者：Wang, YZ
  摘要：In this article we study function estimation via wavelet shrinkage for data with long-range dependence. We propose a fractional Gaussian noise model to approximate nonparametric regression with long-range dependence and establish asymptotics for minimax risks. Because of long-range dependence, the minimax risk and the minimax linear risk converge to 0 at rates that differ from those for data with independence or short-range dependence. Wavelet estimates with best selection of resolution level-...
• Asymptotic properties of estimators for autoregressive models with errors in variables AI翻译
  作者：Chanda, KC
  摘要：Let {X(t), t is an element of Z} be an observable strictly stationary sequence of random variables and let X(t) = U-t + epsilon(t), where {U-t} is an AR (p) and {epsilon(t)} is a strictly stationary sequence representing errors of measurement in {X(t)}, with E{epsilon(1)} = 0. Under some broad assumptions on {epsilon(t)} We establish the consistency properties as well as the rates of convergence for the standard estimators for the autoregressive parameters computed from a set of modified Yule-...
• Direct use of regression quantiles to construct confidence sets in linear models AI翻译
  作者：Zhou, KQ; Portnoy, SL
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Direct use of the empirical quantile function provides a standard distribution-free approach to constructing confidence intervals and confidence bands for population quantiles. We apply this method to construct confidence intervals and confidence bands for regression quantiles and to construct prediction intervals based on sample regression quantiles. Comparison of the direct method with the studentization and the bootstrap methods are discussed. Simulation results show that the direct method ...
• Consistency for the least squares estimator in nonparametric regression AI翻译
  作者：VandeGeer, S; Wegkamp, M
  摘要：We shall study the general regression model Y = g(0)(X) + epsilon, where X and epsilon are independent. The available information about g(0) can be expressed by g(0) is an element of G for some class G. As an estimator of g(0) we choose the least squares estimator. We shall give necessary and sufficient conditions for consistency of this estimator in terms of(basically) geometric properties of G. Our main tool will be the theory of empirical processes.
• Asymptotic optimality of data-driven Neyman's tests for uniformity AI翻译
  作者：Inglot, T; Ledwina, T
  摘要：Data-driven Neyman's tests resulting from a combination of Neyman's smooth tests for uniformity and Schwarz's selection are investigated. Asymptotic intermediate efficiency of those test with respect to the Neyman-Pearson test is shown to be 1 for a large of converging alternatives. The result shows that data-driven Neyman's tests, contrary to classical goodness-of-fit tests, are indeed omnibus tests adapting well to the data at hand.
• A note on Bayesian c- and D-optimal designs in nonlinear regression models AI翻译
  作者：Dette, H
  作者单位：Technische Universitat Dresden
  摘要：We present a version of Elfving's theorem for the Bayesian D-optimality criterion in nonlinear regression models. The Bayesian optimal design can be characterized as a design which allows a representation of a (uniquely determined) boundary point of a convex subset of L(2)-integrable functions. A similar characterization is given for the Bayesian c-optimality criterion where a (possible) nonlinear function of the unknown parameters has to be estimated. The results are illustrated in the exampl...
• Epi-convergence of sequences of normal integrands and strong consistency of the maximum likelihood estimator AI翻译
  作者：Hess, C
  摘要：Using the variational properties of epi-convergence together with suitable results on the measurability of multifunctions and integrands, we prove a strong law of large numbers for sequences of integrands from which we deduce a general theorem of almost sure convergence (strong consistency) for the maximum likelihood estimator.
• Higher-order approximations to conditional distribution functions AI翻译
  作者：Kolassa, JE
  作者单位：University of Rochester
  摘要：This paper derives higher-order terms in the double-saddlepoint expansion of Skovgaard for a unidimensional conditional cumulative distribution function. Expansions for continuous and lattice random variables are derived. Results are applied to the sufficient statistic in logistic regression.
• Searching probabilities for nonzero effects in search designs for the noisy case AI翻译
  作者：Shirakura, T; Takahashi, T; Srivastava, JN
  作者单位：Colorado State University System; Colorado State University Fort Collins
  摘要：Consider search designs for identifying at most k nonzero effects under a search linear model. In the noisy case, the procedure for identifying these effects, which is based on the sum of squares due to error, is considered and its stochastic properties are studied. The errors of observations are assumed to be distributed independently with the normal distribution N(0, sigma(2)) and, in particular, the case of k = 1 is considered. Some results on a searching probability under the procedure are...
• Asymptotics for the maximum of half-normal plots AI翻译
  作者：Goldstein, L; Gordon, L
  摘要：We study approximations to the distribution of the largest Walsh-Fourier coefficient obtained by transforming i.i.d. observations from a non-normal parent distribution. The study is inspired by the use of half-normal plots in screening studies and in the analysis of molecular sequence data. In the latter context, we indicate why continuity corrections are needed.