1999

最新文章

• Nonparametric Bayesian estimators for counting processes AI翻译
  作者：Kim, YD
  作者单位：Hankuk University Foreign Studies
  摘要：This paper is concerned with nonparametric Bayesian inference of the Aalen's multiplicative counting process model. For a desired nonparametric prior distribution of the cumulative intensity function, a dass of Levy processes is considered, and it is shown that the class of Levy processes is conjugate for the multiplicative counting process model, and formulas for obtaining a posterior process are derived. Finally, our results are applied to several practically important models such as one poi...
• Convergence of a stochastic approximation version of the EM algorithm AI翻译
  作者：Delyon, B; Lavielle, V; Moulines, E
  作者单位：Inria; Universite Paris Cite; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); Centre National de la Recherche Scientifique (CNRS)
  摘要：The expectation-maximization (EM) algorithm isa powerful computational technique for locating maxima of functions. It is widely used in statistics for maximum likelihood or maximum a posteriori estimation in incomplete data models. In certain situations, however, this method is not applicable because the expectation step cannot be performed in closed form. To deal with these problems, a novel method is introduced, the stochastic approximation EM (SAEM), which replaces the expectation step of t...
• On the multivariate runs test AI翻译
  作者：Henze, N; Penrose, MD
  作者单位：Helmholtz Association; Karlsruhe Institute of Technology; Durham University
  摘要：For independent d-variate random variables X-1,...,X-m with common density f and Y-1,...; Y-n with common density g, let R-m,R-n be the number of edges in the minimal spanning tree with vertices X-1,..., X-m, Y-1,...,Y-n that connect points from different samples. Friedman and Rafsky conjectured that a test of H-o: f = g that rejects H-o for small values of R-m,R-n should have power against general alternatives. We prove that R-m,R-n is asymptotically distribution-free under H-o, and that the ...
• Asymptotic normality of the L1 error of the Grenander estimator AI翻译
  作者：Groeneboom, P; Hooghiemstra, G; Lopuhaä, HP
  作者单位：Delft University of Technology
  摘要：Groeneboom introduced a jump process that can be used (among other things) to study the asymptotic properties of the Grenander estimator of a monotone density. In this paper we derive the asymptotic normality of a suitably rescaled version of the L-1 error of the Grenander estimator, using properties of this jump process.
• Multivariate analysis by data depth - Discussion AI翻译
  作者：Hettmansperger, TP; Oja, H; Visuri, S
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Jyvaskyla; Tampere University
• Asymptotic distribution of the reduced rank regression estimator under general conditions AI翻译
  作者：Anderson, TW
  作者单位：Stanford University
  摘要：In the regression model Y = eta + BX + Z with Z unobserved, EZ = 0 and EZZ' = Sigma(ZZ), the least squares estimator of B is (B) over cap = SYXSXX-1. If the rank of B is known to be k less than the dimensions of Y and X, the reduced rank regression estimator of B, say (B) over cap(k), depends on the first k canonical variates of Y and X. The asymptotic distribution of (B) over cap(k) is obtained and compared with the asymptotic distribution of (B) over cap(k). The advantage of (B) over cap(k) ...
• Wedgelets: Nearly minimax estimation of edges AI翻译
  作者：Donoho, DL
  作者单位：Stanford University
  摘要：We study a simple horizon model for the problem of recovering an image from noisy data; in this model the image has an edge with alpha-Holder regularity. Adopting the viewpoint of computational harmonic analysis, we develop an overcomplete collection of atoms called wedgelets, dyadically organized indicator functions with a variety of locations, scales and orientations. The wedgelet representation provides nearly optimal representations of objects in the horizon model, as measured by minimax d...
• Optimal convergence rates for Good's nonparametric maximum likelihood density estimator AI翻译
  作者：Eggermont, PPB; LaRiccia, VN
  作者单位：University of Delaware
  摘要：We study maximum penalized likelihood density estimation using the first roughness penalty functional of Good. We prove a simple pointwise comparison result with a kernel estimator based on the two-sided exponential kernel. This leads to L(1) convergence results similar to those for kernel estimators. We also prove Hellinger distance bounds for the roughness penalized estimator.
• Hierarchical mixtures-of-experts for exponential family regression models: Approximation and maximum likelihood estimation AI翻译
  作者：Jiang, WX; Tanner, MA
  作者单位：Northwestern University
  摘要：We consider hierarchical mixtures-of-experts (HME) models where exponential family regression models with generalized linear mean functions of the form psi(alpha + x(T)beta) are mixed. Here psi(.) is the inverse link function. Suppose the true response y follows an exponential family regression model with mean function belonging to a class of smooth functions of the form psi(h(x)) where h(.) is an element of W-2; K0(infinity) (a Sobolev class over [0, 1](s)). It is shown that the HME probabili...
• A characterization of a neutral to the right prior via an extension of Johnson's sufficientness postulate AI翻译
  作者：Walker, S; Muliere, P
  作者单位：Imperial College London; University of Pavia
  摘要：In this paper we present a new characterization and perspective on a neutral to the right prior. This characterization is based on a sequence of predictive laws which provides explicitly the posterior parameters and Bayes estimators for such a prior.