On improving convergence rate of Bernstein polynomial density estimator

This paper is concerned with the Bernstein estimator [Vitale, R.A. (1975), ‘A Bernstein Polynomial Approach to Density Function Estimation’, in Statistical Inference and Related Topics, ed. M.L. Puri, 2, New York: Academic Press, pp. 87–99] to estimate a density with support [0, 1]. One of the major contributions of this paper is an application of a multiplicative bias correction [Terrell, G.R., and Scott, D.W. (1980), ‘On Improving Convergence Rates for Nonnegative Kernel Density Estimators’, The Annals of Statistics, 8, 1160–1163], which was originally developed for the standard kernel estimator. Moreover, the renormalised multiplicative bias corrected Bernstein estimator is studied rigorously. The mean squared error (MSE) in the interior and mean integrated squared error of the resulting bias corrected Bernstein estimators as well as the additive bias corrected Bernstein estimator [Leblanc, A. (2010), ‘A Bias-reduced Approach to Density Estimation Using Bernstein Polynomials’, Journal of Nonparametric Statistics, 22, 459–475] are shown to be O(n^?8/9) when the underlying density has a fourth-order derivative, where n is the sample size. The condition under which the MSE near the boundary is O(n^?8/9) is also discussed. Finally, numerical studies based on both simulated and real data sets are presented.     

Approximate solution of a profit maximization constrained virtual business planning problem

A virtual business problem is studied, in which a company-contractor outsources production to specialized subcontractors. Finances of the contractor and resource capacities of subcontractors are limited. The objective is to select subcontractors and distribute a part of the demanded production among them so that the profit of the contractor is maximized. A generalization of the knapsack problem, called Knapsack-of-Knapsacks (K-of-K), is used to model this situation, in which items have to be packed into small knapsacks and small knapsacks have to be packed into a large knapsack. A fully polynomial time approximation scheme is developed to solve the problem K-of-K.     

算子多项式的数值域

研究Hilbert空间H上的算子多项式数值域的性质及算子多项式数值域与n-次数值域之间的关系。     

关于概率论与数理统计的两点注记

本文对正态随机变量的线性组合的分布，对正态总体的样本极差与标准差之问的关系，作了两点评注。     

常微分方程组的两个新的可积类型

本文给出了两类常微分方程组的求解法，论证了它们的可积性．     

Polynomial approximations of multivariate smooth functions from quasi-random data

We improve a Monte Carlo algorithm which computes accurate approximations of smooth functions on multidimensional Tchebychef polynomials by using quasi-random sequences. We first show that the convergence of the previous algorithm is twice faster using these sequences. Then, we slightly modify this algorithm to make it work from a single set of random or quasi-random points. This especially leads to a Quasi-Monte Carlo method with an increased rate of convergence for numerical integration.     

具有任意激励的非齐次线性自治系统的齐次扩容精细算法

基于第二类Chebyshev多项式函数系的特点与齐次扩容技巧,设计了求解非齐次线性自治系统的一种新的长效精细算法(HHPD CS).其不仅避免了HPD F算法中的矩阵求逆,还克服了HH-PD F算法中对右端激励的周期性要求,从而适合于任意形式的右端激励.理论与算例表明,长效HHPD CS算法十分有效,不仅计算量比R K算法小许多,而且数值稳定、计算精度高、设计合理,易于推广和实现.     

Automatic polynomial wavelet regression

In Oh, Naveau and Lee (2001) a simple method is proposed for reducing the bias at the boundaries for wavelet thresholding regression. The idea is to model the regression function as a sum of wavelet basis functions and a low-order polynomial. The latter is expected to account for the boundary problem. Practical implementation of this method requires the choice of the order of the low-order polynomial, as well as the wavelet thresholding value. This paper proposes two automatic methods for making such choices. Finite sample performances of these two methods are evaluated via numerical experiments.     

函数的渐近多项式

引入函数的渐近多项式，并给出了其系数计算公式。