截面数据插值的组合模型及其实证

采用Moran指标分析截面数据空间自相关性.针对具有空间自相关截面数据缺失插值问题,分别建立一阶空间自回归插值模型与克立格方法插值模型,在此基础上建立截面数据的组合插值模型,并用信息熵法确定组合插值模型的加权系数.用2003年福建部分市县城镇化水平的截面数据建立插值模型实证研究结果表明:组合插值模型的效果优于单项插值模型的效果.     

Kriging空间分析法及其在城市大气污染中的应用

目前常用的大气污染物浓度插值方法没有考虑变量的空间分布特征 ,无法检验插值结果 ,不适用于复杂的城市大气环境的空间分析 .本文引用非参数地质统计学的指示克立格法 ,定义了大气环境研究领域的指示函数 ,据此分析了某市 SO2 的空间变异特征 ,估计了其 SO2 的空间分布 ,并给出了估计的精度 (估计误差 ) ,最后研究了监测点位置与估计误差空间分布之间的关系 .结果表明指示克立格法是城市大气污染空间分析、插值的有力工具 .     

泛逻辑的零级泛运算模型的代数性质

讨论泛逻辑的零级泛运算模型的基本代数性质。证明T（x,y,^）是阿基米德型三角范数;泛与运算模型与泛蕴涵运算模型形成一个伴随对;当h∈（0,0．75）时,有界格（[0,1],∨,∧,,＊,→0,1）做成一个MV-代数;当h∈（0．75,1）时,有界格（[0,1],∨,∧,＊,→0,1）做成一个乘积代数。进一步,给出了零级泛与运算模型与泛或运算模型的加性生成元与乘性生成元。     

软泛代数及其整体结构性质

提出了软泛代数概念,将已有的软群、软环等概念统一纳入这一框架中,从整体上研究了软泛代数的序结构性质,证明了固定指标集和T-代数后,相应的软T-代数全体以点式序形成代数格.引入了Scott连续软泛代数概念,证明了从代数紧拓扑空间到给定T-代数的Scott连续软T-代数的全体以点式序形成代数格.     

S_2~1(△_(mn)~((2)))上的整节点插值

[1]中提出一种二元样条的插值方法,后来[2]对此种方法进行了较深入的分析.[2]中区分了二种不同类型的插值点:基本插值点和附加插值点;也给出了两种不同类型的插值:整节点插值和半整节点插值。本文研究空间S_2~1(△_(mn)~((2)))上的整节点插值,讨论插值     

自反算子代数的模和交换子以及一阶上同调空间

设L是赋范线性空间上的子空间格,一个子空间是自反AlgL-模的充分必要条件被得到,当L是完全分配子空间格时,自反AlgL-模的二次交换子被描述,进而,本文引入V-生成子稠格,这是一种严格地包含了完全分配格和五角格的格类。当L是可换的V-生成子稠格时,模模交换子C(AlgL;M)和代数AlgLatM都被分解成直和,并且满足条件H~1(AlgL,B(H))=0的一阶上同调空间H~1(AlgL,M)被刻划。     

复插值逼近

本文着重地介绍复数域上各类插值多项式(有时也提到有理函数)的收敛性与发散性问题的近代成果,适当地介绍了复插值逼近阶的估计。文章共分七节,其中§1为问题的提出;§2介绍紧集上解析函数的Lagrange插值多项式收敛的充要条件;§3介绍A(|z|≤1)上函数的Lagrange插值的收敛及发散问题;§4是一般区域上Lagrange插值的收敛问题;§5介绍调和多项式的插值;§6介绍Hermite及Hermite-Fejer插值的收敛与发散性问题;§7介绍有理函数插值的收敛性问题。     

W空间中最佳逼近插值算子

一元函数有种种不同的插值方法，如多项式插值，样条插值，有理插值等，也给出了最佳插值算子[2]本文对二元函数讨论最佳逼近插值算子．设X是点集Ω上的实函数空间，是Ω上给定的一组点．由下式确定x上的一组泛函设Xu是X的。维子空间，定义X到Xu的算子Hn：其中（a；闪丹ZCXn．对X的子集人称dA【VI“fill＿fillSlipwIVJ一【＊un八V川UJXnCX｛。。（Q）IVC。。irCh为A的逼近偏差．若某个n维子空IWXu达到（2）式的第一个下确界，则称此Xn为A的最佳逼近子空间，记为X：．X：中达到（2）式的第二个下确界的扣；（Q）｝Z称为A的最…     

Bergman空间的插值多项式逼近

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基于三次卷积插值的贝叶斯滤波方法研究

针对一类二维空间系统的状态估计模型,提出了一种用三次卷积插值方法递推估计的非线性滤波算法.仿真实例采用一个常用的非线性模型,并与粒子滤波算法进行对比分析,仿真结果表明三次卷积插值方法提高滤波估计精度,从而验证其估计一类状态估计模型解析解的可行性,其插值算法还可以推广到多维空间系统.     

Evidence of self-organized criticality in insect populations

Self-organized criticality (SOC) occurs in systems consisting of a substrate or medium which can be locally stressed to a critical state. When the critical threshold is exceeded, the stress is distributed to the neighborhood around the locale, which can lead to critical states in the neighboring locations thereby propagating the disturbance. The SOC system manifests its behavior in distinctive temporal and spatial relationships. Diagnostic behaviors include conformity of dynamics to a power law (F(s) = βs^α) relating the frequency distribution function to the observed behavior (1/f noise) and fractal behavior or spatial self-similarity. We analyzed 40 to 50 years of grasshopper infestations in Idaho and Montana for evidence of SOC. In both states, grasshopper infestations in four of the seven ecoregions statistically fit (the expected frequencies matched the observed with a probability of >95%) the power law relationship. In ecoregions where the model failed to fit the observed behavior, grasshopper infestations were typically rare and small scale. Spatial self-similarity was evident from graphical and statistical analyses. In both states there were statistically significant levels of concordance (among ranks) and correlation (among areas of infestations) between ecoregions. The spatial distributions of grasshopper infestations over time also showed considerable overlaps between ecoregions. However, only in Idaho were the variances in grasshopper infestations among ecoregions homogeneous. If rangeland grasshopper populations are SOC systems, then the management of outbreaks may need to be dramatically restructured. This system appears to be an example of “complex complexity,” in which multiple dynamics (i.e., the appearance of SOC with partial blockage of outbreaks, catastrophic dynamics, and perhaps chaos) give rise to a rich set of behaviors which arise at different scales and certainly necessitate further theoretical developments in order to understand. © 1997 John Wiley & Sons, Inc.     

Asymptotic theory of parameter estimation by a contrast function based on interpolation error

Interpolation is an important issue for a variety fields of statistics (e.g., missing data analysis). In time series analysis, the best interpolator for missing points problem has been investigated in several ways. In this paper, the asymptotics of a contrast function estimator defined by pseudo interpolation error for stationary process are investigated. We estimate parameters of the process by minimizing the pseudo interpolation error written in terms of a fitted parametric spectral density and the periodogram based on observed stretch. The estimator has the consistency and asymptotical normality. Although the criterion for the interpolation problem is known as the best in the sense of smallest mean square error for past and future extrapolation, it is shown that the estimator is asymptotically inefficient in general parameter estimation, which leads to an unexpected result.     

Variational image inpainting

Inpainting is an image interpolation problem with broad applications in image and vision analysis. Described in the current expository paper are our recent efforts in developing universal inpainting models based on the Bayesian and variational principles. Discussed in detail are several variational inpainting models built upon geometric image models, the associated Euler‐Lagrange PDEs and their geometric and dynamic interpretations, as well as effective computational approaches. Novel efforts are then made to further extend this systematic variational framework to the inpainting of oscillatory textures, interpolation of missing wavelet coefficients as in the wireless transmission of JPEG2000 images, as well as light‐adapted inpainting schemes motivated by Weber's law in visual perception. All these efforts lead to the conclusion that unlike many familiar image processors such as denoising, segmentation, and compression, the performance of a variational/Bayesian inpainting scheme much more crucially depends on whether the image prior model well resolves the spatial coupling (or geometric correlation) of image features. As a highlight, we show that the Besov image models appear to be less interesting for image inpainting in the wavelet domain, highly contrary to their significant roles in thresholding‐based denoising and compression. Thus geometry is the single most important keyword throughout this paper. © 2005 Wiley Periodicals, Inc.     

基于插值机理的分布参数系统T-S模糊推理建模

研究分布参数系统的建模问题,给出一种基于T-S模糊推理与HX方法的T-S型模糊三角插值推理建模方法,满足插值机理.理论分析表明方法可利用有限的采样数据推断分布参数系统的时空变化规律.仿真实验显示方法是有效的,且具有建模精度较高的特点.     

A neural network approach to efficient valuation of large portfolios of variable annuities

Managing and hedging the risks associated with Variable Annuity (VA) products require intraday valuation of key risk metrics for these products. The complex structure of VA products and computational complexity of their accurate evaluation have compelled insurance companies to adopt Monte Carlo (MC) simulations to value their large portfolios of VA products. Because the MC simulations are computationally demanding, especially for intraday valuations, insurance companies need more efficient valuation techniques. Recently, a framework based on traditional spatial interpolation techniques has been proposed that can significantly decrease the computational complexity of MC simulation (Gan and Lin, 2015). However, traditional interpolation techniques require the definition of a distance function that can significantly impact their accuracy. Moreover, none of the traditional spatial interpolation techniques provide all of the key properties of accuracy, efficiency, and granularity (Hejazi et al., 2015). In this paper, we present a neural network approach for the spatial interpolation framework that affords an efficient way to find an effective distance function. The proposed approach is accurate, efficient, and provides an accurate granular view of the input portfolio. Our numerical experiments illustrate the superiority of the performance of the proposed neural network approach compared to the traditional spatial interpolation schemes.     

插值法在数据修正中的应用

为了使评估的结果达到某种规定的水平,本文研究了运用线性插值、拉格朗日插值以及牛顿插值方法对某公司员工考核数据按照一定的规则进行了修正,同时,对各种方法的修正前、后的结果做了比较．结果表明拉格朗日插值法效果最好,但是计算量偏大;线性插值法虽然效果一般,但是计算复杂度却较低;而牛顿插值法达不到我们预期的效果．     

Interpolation by universal,hypercyclic functions

In the present article we study an interpolation problem for classes of analytic functions, in a systematic manner. More precisely, we provide sufficient conditions so that proper and “big”, in the Baire category sense, subclasses of analytic functions have an interpolation property at an infinite set of points. We then apply our main theorems to several classes of universal, hypercyclic functions.     

The best quadrature formula based on Hermite information for the class <Emphasis Type="Italic">KW</Emphasis><Superscript>2</Superscript> [<Emphasis Type="Italic">a,b</Emphasis>]

The best quadrature formula has been found in the following sense: for a function whose norm of the second derivative is bounded by a given constant and the best quadrature formula for the approximate evaluation of integration of that function can minimize the worst possible error if the values of the function and its derivative at certain nodes are known. The best interpolation formula used to get the quadrature formula above is also found. Moreover, we compare the best quadrature formula with the open compound corrected trapezoidal formula by theoretical analysis and stochastic experiments.