3期

最新文章

• Robust inference for variance components models for single trees of cell lineage data AI翻译
  作者：Huggins, RM
  摘要：Previously, Huggins and Staudte examined robust estimators for a variance components formulation of the bifurcating autoregressive model for cell lineage data. They gave asymptotic properties of the estimators ifa large number of trees were observed. However, for single trees the derivation of these asymptotic properties is more complex. Here the asymptotic distributions of robust estimators of parameters associated with the stationary bifurcating autoregressive process as a single tree become...
• 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 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...
• 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...
• On the existence of inferences which are consistent with a given model AI翻译
  作者：Berti, P; Rigo, P
  摘要：If {p(theta)} is a sigma-additive statistical model and pi a finitely additive prior, then any statistic T is sufficient, with respect to a suitable inference consistent with {p(theta)} and pi, provided only that p(theta)(T = t) = 0 for all theta and t. Here, sufficiency is to be intended in the Bayesian sense, and consistency in the sense of Lane and Sudderth. As a corollary, if {p(theta)} is sigma-additive and diffuse, then, whatever the prior pi, there is an inference which is consistent wi...
• Choice of hierarchical priors: Admissibility in estimation of normal means AI翻译
  作者：Berger, JO; Strawderman, WE
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：In hierarchical Bayesian modeling of normal means, it is common to complete the prior specification by choosing a constant prior density for unmodeled hyperparameters (e.g., variances and highest-level means). This common practice often results in an inadequate overall prior, inadequate in the sense that estimators resulting from its use can be inadmissible under quadratic loss. In this paper, hierarchical priors for normal means are categorized in terms of admissibility and inadmissibility of...
• Constrained M-estimation for multivariate location and scatter AI翻译
  作者：Kent, JT; Tyler, DE
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：Consider the problem of estimating the location vector and scatter matrix from a set of multivariate data. Two standard classes of robust estimates are M-estimates and S-estimates. The M-estimates can be tuned to give good local robustness properties, such as good efficiency and a good bound on the influence function at an underlying distribution such as the multivariate normal. However, M-estimates suffer from poor breakdown properties in high dimensions. On the other hand, S-estimates can be...
• Complete order statistics in parametric models AI翻译
  作者：Mattner, L
  摘要：For a given statistical model P it may happen that the order statistic is complete for each IID model based on P. After reviewing known relevant results for large nonparametric models and pointing out generalizations to small nonparametric models, we essentially prove that this happens generically even in smooth parametric models. As a consequence it may be argued that any statistic depending symmetrically on the observations can be regarded as an optimal unbiased estimator of its expectation....