The Annals of Statistics

最新文章

• On nonparametric regression for iid observations in a general setting AI翻译
  作者：Efromovich, S
  摘要：we consider the problem of sharp-optimal estimation of a response function f(x) in a random design nonparametric regression under a general model where a pair of observations (Y, X) has a joint density p(y, x) = p(y\f(x))pi(x). We wish to estimate the response function with optimal minimax mean integrated squared error convergence as the sample size tends to infinity. Traditional regularity assumptions on the conditional density p(y\theta) assumed for parameter theta estimation are sufficient ...
• A robust adjustment of the profile likelihood AI翻译
  作者：Stafford, JE
  摘要：Under mild misspecifications of model assumptions, maximum likelihood estimates often remain consistent and asymptotically normal. Asymptotic normality will often hold for the signed root of the likelihood ratio statistic and the score statistic as well. However, standard estimates of asymptotic variance are usually inconsistent. This occurs when Bartlett's second identity fails. In the manner of McCullagh and Tibshirani, a variance correction may be used to adjust the profile likelihood so th...
• Using specially designed exponential families for density estimation AI翻译
  作者：Efron, B; Tibshirani, R
  摘要：We wish to estimate the probability density g(y) that produced an observed random sample of vectors y(1), y(2),..., y(n). Estimates of g(y) are traditionally constructed in two quite different ways: by maximum likelihood fitting within some parametric family such as the normal or by nonparametric methods such as kernel density estimation. These two methods can be combined by putting an exponential family ''through'' a kernel estimator. These are the specially designed exponential families ment...
• Blind deconvolution of discrete linear systems AI翻译
  作者：Gamboa, F; Gassiat, E
  作者单位：Universite Paris Saclay
  摘要：We study the blind deconvolution problem in the case where the input noise has a finite discrete support and the transfer linear system is not necessarily minimum phase. We propose a new family of estimators built using algebraic considerations. The estimates are consistent under very wide assumptions: The input signal need not be independently distributed; the cardinality of the finite support may be estimated simultaneously. We consider in particular AR systems: In this case, we prove that t...
• A combinatorial central limit theorem for randomized orthogonal array sampling designs AI翻译
  作者：Loh, WL
  作者单位：Purdue University System; Purdue University
  摘要：Let X be a random vector uniformly distributed on the unit cube and f: [0, 1](3) --> R be a measurable function. An objective of many computer experiments is to estimate mu = E(f circle X) by computing f at a set of points in [0, 1](3). There is a design issue in choosing these points. Recently Owen and Tang independently suggested using randomized orthogonal arrays in the choice of such a set. This paper investigates the convergence rate to normality of the distribution of the average of a se...
• Sequential estimation for the autocorrelations of linear processes AI翻译
  作者：Lee, S
  摘要：This paper considers sequential point estimation of the autocorrelations of stationary linear processes within the framework of the sequential procedure initiated by Robbins. The sequential estimator proposed here is based on the usual sample autocorrelations and is shown to be risk efficient in the sense of Starr as the cost per observation approaches zero. To achieve the asymptotic risk efficiency, we are led to study the uniform integrability and random central limit theorem of the sample a...
• The Bahadur-Kiefer representation for U-quantiles AI翻译
  作者：Arcones, MA
  摘要：We consider the distributional and the almost sure pointwise Bahadur-Kiefer representation for U-quantiles. We show that the order of this representation depends on the order of the local variance of the empirical process of U-statistic structure at the U-quantile. Our results indicate that U-quantiles can be smoother than quantiles. U-quantiles can either be as unsmooth as quantiles or can behave as differentiable statistical functionals.
• Linear rank statistics, finite sampling, permutation tests and Winsorizing AI翻译
  作者：Shorack, GR
  摘要：Asymptotic normality and a representation of all possible subsequential limiting distributions of a simple linear rank statistic are obtained. This is then applied to finite sampling and permutation teats for slope coefficients. The effects of Winsorizing in these situations are considered carefully. Of particular interest regarding slope coefficients is that either using normal score regression constants or Winsorizing slowly increasing numbers of the population values will guarantee asymptot...
• Consistency of data-driven histogram methods for density estimation and classification AI翻译
  作者：Lugosi, G; Nobel, A
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
  摘要：We present general sufficient conditions for the almost sure L-1-consistency of histogram density estimates based on data-dependent partitions. Analogous conditions guarantee the almost-sure risk consistency of histogram classification schemes based on data-dependent partitions. Multivariate data are considered throughout. In each case, the desired consistency requires shrinking cells, subexponential growth of a combinatorial complexity measure and sublinear growth of the number of cells. It i...
• On the convergence of the Markov chain simulation method AI翻译
  作者：Athreya, KB; Doss, H; Sethuraman, J
  作者单位：University System of Ohio; Ohio State University; State University System of Florida; Florida State University; State University System of Florida; Florida State University
  摘要：The Markov chain simulation method has been successfully used in many problems, including some that arise in Bayesian statistics. We give a self-contained proof of the convergence of this method in general state spaces under conditions that are easy to verify.