1999

最新文章

• Theoretical comparisons of block bootstrap methods AI翻译
  作者：Lahiri, SN
  作者单位：Iowa State University
  摘要：In this paper, we compare the asymptotic behavior of some common block bootstrap methods based on nonrandom as well as random block lengths. It is shown that, asymptotically, bootstrap estimators derived using any of the methods considered in the paper have the same amount of bias to the first order. However, the variances of these bootstrap estimators may he different even in the first order. Expansions for the bias, the variance and the mean-squared error of different block bootstrap varianc...
• Balance and orthogonality in designs for mixed classification models AI翻译
  作者：Vanleeuwen, DM; Birkes, DS; Seely, JF
  作者单位：New Mexico State University; Oregon State University
  摘要：A classification model is easiest to analyze when it has a balanced design. Many of the nice features of balanced designs are retained by error-orthogonal designs, which were introduced in a recent paper by the authors. The present paper defines a kind of partially balanced design and shows that this pal tial balance is sufficient to ensure the error-orthogonality of a mixed classification model. Results are provided that make the partial balance condition easy to check. It is shown that, for ...
• Concentration and goodness-of-fit in higher dimensions: (Asymptotically) distribution-free methods AI翻译
  作者：Polonik, W
  作者单位：University of California System; University of California Davis
  摘要：A novel approach for constructing goodness-of-fit techniques in arbitrary (finite) dimensions is presented. Testing problems are considered as well as the construction of diagnostic plots. The approach is based on some new notions of mass concentration, and in fact, our basic testing problems are formulated as problems of goodness-of-concentration. It is this connection to concentration of measure that makes the approach conceptually simple. The presented test statistics are continuous functio...
• A central limit theorem for multivariate generalized trimmed k-means AI翻译
  作者：García-Escudero, LA; Gordaliza, A; Matrán, C
  作者单位：Universidad de Valladolid
  摘要：A central limit theorem for generalized trimmed h-means is obtained in a very general framework that covers the multivariate setting, general penalty functions and general k greater than or equal to 1. Several applications, including the location estimator case (k = 1) for elliptical distributions and the construction of multivariate (not necessarily connected) tolerance zones, are also given.
• Bandwidth selection: Classical or plug-in? AI翻译
  作者：Loader, CR
  作者单位：Alcatel-Lucent; Lucent Technologies
  摘要：Bandwidth selection for procedures such as kernel density estimation and local regression have been widely studied over the past decade. Substantial evidence has been collected to establish superior performance of modern plug-in methods in comparison to methods such as cross validation: this has ranged from detailed analysis of rates of convergence, to simulations, to superior performance on real datasets. In this work we take a detailed look at some of this evidence, looking into the sources ...
• Testing uniformity versus a monotone density AI翻译
  作者：Woodroofe, M; Sun, JY
  作者单位：University of Michigan System; University of Michigan; University System of Ohio; Case Western Reserve University
  摘要：The paper is concerned with testing uniformity versus a monotone density. This problem arises in two important contexts, after transformations, testing whether a sample is a simple random sample or a biased sample, and testing whether the intensity function of a nonhomogeneous Poisson process is constant against monotone alternatives. A penalized likelihood ratio test (P-test) and a Dip likelihood test (D-test) are developed. The D-test is analogous to Hartigan and Hartigan's (1985) Pip test f...
• Binomial mixtures: Geometric estimation of the mixing distribution AI翻译
  作者：Wood, GR
  作者单位：Massey University
  摘要：Given a mixture of binomial distributions, how do we estimate the unknown mixing distribution? We build on earlier work of Lindsay and further elucidate the geometry underlying this question, exploring the approximating role played by cyclic polytopes. Convergence of a resulting maximum likelihood fitting algorithm is proved and numerical examples given; problems over the lack of identifiability of the mixing distribution in part disappear.
• Locally lattice sampling designs for isotropic random (vol 23, pg 1991, 1995) AI翻译
  作者：Stein, ML
  作者单位：University of Chicago
• The consistency of posterior distributions in nonparametric problems AI翻译
  作者：Barron, A; Schervish, MJ; Wasserman, L
  作者单位：Yale University; Carnegie Mellon University
  摘要：We give conditions that guarantee that the posterior probability of every Hellinger neighborhood of the true distribution tends to 1 almost surely. The conditions are (1) a requirement that the prior not put high mass near distributions with very rough densities and (2) a requirement that the prior put positive mass in Kullback-Leibler neighborhoods of the true distribution. The results are based on the idea of approximating the set of distributions with a finite-dimensional set of distributio...
• The limiting behavior of a modified maximal symmetric 2s-spacing with applications AI翻译
  作者：Swanepoel, JWH
  作者单位：North West University - South Africa
  摘要：This paper presents a solution to an open problem in astrophysics, namely that of estimating nonparametrically the strength of the pulsed signal in a series of high-energy photon arrival times. The newly proposed estimator, based on a modified maximal symmetric. 2s-spacing, is shown to be strongly consistent and asymptotically normally distributed, and a Monte Carlo study shows that its small and moderate sample behavior is very satisfactory.. Additionally, new results regarding the weak and s...