Exact and Asymptotically Optimal Bandwidths for Kernel Estimation of Density Functionals

Given a density f we pose the problem of estimating the density functional $\psi_r=\int f^{(r)}f$ for a non-negative even r making use of kernel methods. This is a well-known problem but some of its features remained unexplored. We focus on the problem of bandwidth selection. Whereas all the previous studies concentrate on an asymptotically optimal bandwidth here we study the properties of exact, non-asymptotic ones, and relate them with the former. Our main conclusion is that, despite being asymptotically equivalent, for realistic sample sizes much is lost by using the asymptotically optimal bandwidth. In contrast, as a target for data-driven selectors we propose another bandwidth which retains the small sample performance of the exact one.     

Strichartz Estimates on Asymptotically de Sitter Spaces

In this article we prove a family of local (in time) weighted Strichartz estimates with derivative losses for the Klein–Gordon equation on asymptotically de Sitter spaces and provide a heuristic argument for the non-existence of a global dispersive estimate on these spaces. The weights in the estimates depend on the mass parameter and disappear in the “large mass” regime. We also provide an application of these estimates to establish small-data global existence for a class of semilinear equations on these spaces.     

渐近最优的经验贝叶斯决策

本文获得了刻度指数族变量带误差情形下的贝叶斯决策,且利用解卷积的核方法构造出了经验贝叶斯决策.在适当的条件下,证明了经验贝叶斯决策的渐近最优性.     

Asymptotically Optimal Weighted Numerical Integration

We study numerical integration of Hölder-type functions with respect to weights on the real line. Our study extends previous work by F. Curbera (J. Complexity14(1), (1998) and relies on a connection between this problem and the approximation of distribution functions by empirical ones. As an application we reproduce a variant of the well-known result for weighted integration of Brownian paths.     

渐近最优的经验贝叶斯决策

本文在加权线性损失下讨论一类广义指数分布刻度参数的经验贝叶斯检验问题.利用核密度估计函数构造单调的经验贝叶斯检验函数,在适当的条件下证明所构造的检验函数的渐近最优性并获得其收敛速度.该收敛速度可以任意接近O(n-1).最后,给出一个例子用以验证本文的主要结果是合理的.     

Resolvent Estimates for the Laplacian on Asymptotically Hyperbolic Manifolds

Combining results of Cardoso-Vodev [6] and Froese-Hislop [9], we use Mourre’s theory to prove high energy estimates for the boundary values of the weighted resolvent of the Laplacian on an asymptotically hyperbolic manifold. We derive estimates involving a class of pseudo-differential weights which are more natural in the asymptotically hyperbolic geometry than the weights used in [6]. submitted 28/04/05, accepted 26/09/05     

Low Frequency Estimates and Local Energy Decay for Asymptotically Euclidean Laplacians

For Riemannian metrics G on ? ^d which are long range perturbations of the flat one, we prove estimates for (? Δ _G  ? λ ?iε)^?n as λ → 0, which are uniform with respect to ε, for all n ≤ [d/2] +1 in odd dimension and n ≤ d/2 in even dimension. We also give applications to the time decay of Schrödinger and Wave (or Klein–Gordon) equations.     

密度核估计的随机加权法

利用随机加权法的思想,找出概率密度函数估计的随机加权统计量,在适当的条件下证明随机加权分布逼近核估计误差分布的精度为     

Optimal Estimates with Moduli of Continuity

In this paper we obtain estimates with optimal constants for the pointwise approximation of functions by linear positive functional, with the aid of a new second order modulus with a parameter, as well as with the aid of the first order modulus of the derivatives of functions.     

Pointwise Improvement of Multivariate Kernel Density Estimates

Multivariate kernel density estimators are known to systematically deviate from the true value near critical points of the density surface. To overcome this difficulty a method based on Rao–Blackwell's theorem is proposed. Local corrections of kernel density estimators are achieved by conditioning these estimators with respect to locally sufficient statistics. The asymptotic as well as the small sample size behavior of the improved estimators are studied. Asymptotic bias and variance are investigated and weak and complete consistency are derived under mild hypothesis.     

Asymptotically Efficient Estimation of the Derivative of the Invariant Density

The problem of estimation of the derivative of the invariant density is considered for a one-dimensional ergodic diffusion process. The lower minimax bound on the L ²-type risk of all estimators is proposed and an asymptotically efficient (up to the constant) in the sense of this bound kernel-type estimator is constructed.     

Asymptotically Efficient Sequential Kernel Estimates of the Drift Coefficient in Ergodic Diffusion Processes

A sequential asymptotically efficient procedure is constructed for estimating the drift coefficient at a given state point in ergodic diffusion processes. Sequential kernel estimators are used. The optimal convergence rate with the sharp constant is given for a local minimax risk.     

混合样本时密度估计的渐近正态性

对多元密度函数的最近邻估计和核估计,在样本为不混合的场合下,本文获得了它们的渐近正态性。     

CONVERGENCE RATE OF MULTIVARIATE K-NEAREST NEIGHBOR DENSITY ESTIMATES

Let be the collection of m-times continuously differentiable probability densities fon R~d such that 丨D~af(x_1)-D~af(x_2)丨≤M‖x_1-x_2‖~β for x_1,x_2∈R~d,[a]=m,where D~adenotes the differential operator defined by D~a=([a])/(x_1~a…x_d~a_d).Under rather weak conditionson K(x),the necessary and sufficient conditions for sup丨_n(x)-f(x)丨=0(((logn/n)~λ/(d+3λ),λ=m+β,f∈ are that ∫x~aK(xi)dx=0 for 0<[a]≤m.Finally the convergenco rate at apoint is given.     

Visualization of Multivariate Density Estimates With Shape Trees

This article introduces graphical tools for visualizing multivariate functions, specializing to the case of visualizing multivariate density estimates. We visualize a density estimate by visualizing a series of its level sets. From each connected part of a level set a shape tree is formed. A shape tree is a tree whose nodes are associated with regions of the level set. With the help of a shape tree we define a transformation of a multivariate set to a univariate function. The shape trees are visualized with the shape plots and the location plot. By studying these plots one may identify the regions of the Euclidean space where the probability mass is concentrated. An application of shape trees to visualize the distribution of stock index returns is presented.     

密度核估计的一致收敛速度

Suppose that X₁,…,X_n are samples drawn from a in-dimensional population with probability density function f belonging to a family C_kα(where k is a given positive integer, and α is a given positive number) defined as follows: f∈C_kα if and only if.     

Asymptotically Local Minimax Estimation of Infinitely Smooth Density with Censored Data

The problem of the nonparametric minimax estimation of an infinitely smooth density at a given point, under random censorship, is considered. We establish the exact asymptotics of the local minimax risk and propose the efficient kernel-type estimator based on the well known Kaplan-Meier estimator.