1997

最新文章

• M-estimation, convexity and quantiles AI翻译
  作者：Koltchinskii, VI
  作者单位：University of New Mexico
  摘要：The paper develops a class of extensions of the univariate quantile function to the multivariate case (M-quantiles), related in a certain way to M-parameters of a probability distribution and their M-estimators. The spatial (geometric) quantiles, recently introduced by Koltchinskii and Dudley and by Chaudhuri as well as the regression quantiles of Koenker and Basset, are the examples of the M-quantile function discussed in the paper. We study the main properties of M-quantiles and develop the ...
• Heavy tail modeling and teletraffic data - Discussion AI翻译
  作者：Adler, RJ
  作者单位：Technion Israel Institute of Technology; University of North Carolina; University of North Carolina Chapel Hill
• 1994 Wald memorial lectures -: Polynomial splines and their tensor products in extended linear modeling -: Discussion AI翻译
  作者：Härdle, W; Marron, JS; Yang, L
  作者单位：Humboldt University of Berlin; University of North Carolina; University of North Carolina Chapel Hill
• On the rate of uniform convergence of the product-limit estimator: Strong and weak laws AI翻译
  作者：Chen, K; Lo, SH
  作者单位：Hong Kong University of Science & Technology; Columbia University
  摘要：By approximating the classical product-limit estimator of a distribution function with an average of iid random variables, we derive sufficient and necessary conditions for the rate of(both strong and weak) uniform convergence of the product-limit estimator over the whole line. These findings somehow fill a longstanding gap in the asymptotic theory of survival analysis. The result suggests a natural way of estimating the rate of convergence. We also prove a related conjecture raised by Gill an...
• A bandwidth selector for local linear density estimators AI翻译
  作者：Cheng, MY
  作者单位：National Chung Cheng University
  摘要：Local linear density estimators achieve automatic boundary corrections and enjoy some typical optimal properties. Proper choice of the smoothing parameters is crucial for their performance. A data-based bandwidth selector is developed in the spirit of plug-in rules. Consistency and asymptotic normality of the selected bandwidth are demonstrated. The bandwidth is very efficient regardless of whether there are non-smooth boundaries in the support of the density or not.
• All estimates with a given risk, riccati differential equations and a new proof of a theorem of Brown AI翻译
  作者：DasGupta, A; Strawderman, WE
  作者单位：Purdue University System; Purdue University; Rutgers University System; Rutgers University New Brunswick
  摘要：For the canonical problem of estimating the mean of a multivariate normal distribution with a known covariance matrix using a squared error loss, we give a general method for finding estimates that have risk functions identical to that of a given inadmissible estimate. In the case of more than one dimension, the estimates considered are spherically symmetric, but in one dimension no such assumption is made. Generally speaking, we characterize all estimates which have the risk duplication prope...
• Data driven smooth tests for composite hypotheses AI翻译
  作者：Inglot, T; Kallenberg, WCM; Ledwina, T
  作者单位：Wroclaw University of Science & Technology; University of Twente
  摘要：The classical problem of testing goodness-of-fit of a parametric family is reconsidered. A new test for this problem is proposed and investigated. The new test statistic is a combination of the smooth test statistic and Schwarz's selection rule. More precisely, as the sample size increases, an increasing family of exponential models describing departures from the null model is introduced and Schwarz's selection rule is presented to select among them. Schwarz's rule provides the right dimension...
• Rates of convergence of estimates, Kolmogorov's entropy and the dimensionality reduction principle in regression AI翻译
  作者：Nicoleris, T; Yatracos, YG
  作者单位：Universite de Montreal
  摘要：L-1-optimal minimum distance estimators are provided for a projection pursuit regression type function with smooth functional components that are either additive or multiplicative, in the presence of or without interactions. The obtained rates of convergence of the estimate to the true parameter depend on Kolmogorov's entropy of the assumed model and confirm Stone's heuristic dimensionality reduction principle. Rates of convergence are also obtained for the error in estimating the derivatives ...
• Simultaneous estimation of location parameters for sign-invariant distributions AI翻译
  作者：Xu, JL
  作者单位：University of Houston System; University of Houston
  摘要：Estimation of p, p greater than or equal to 3, location parameters of a distribution of a p-dimensional random vector X is considered under quadratic loss. Explicit estimators which are better than the best invariant one are given for a sign-invariantly distributed random vector X. The results depend only on the second and the third moments of parallel to X-theta parallel to. The generalizations to concave loss functions and to n observations are also considered. Additionally, if the scale is ...
• A plug-in approach to support estimation AI翻译
  作者：Cuevas, A; Fraiman, R
  作者单位：Autonomous University of Madrid; Universidad de la Republica, Uruguay
  摘要：We suggest a new approach, based on the use of density estimators, for the problem of estimating the (compact) support of a multivariate density. This subject (motivated in terms of pattern analysis by Grenander) has interesting connections with detection and clustering. A natural class of density-based estimators is defined. Universal consistency results and convergence rates are established for these estimators, with respect to the usual measure-based metric d(mu) between sets. Further conve...