污染数据回归分析中参数的最小一乘估计

考虑简单回归模型：ｙｉ＝ｘ′ｉβ＋ｅｉ,ｉ＝１,…,ｎ。其中Ｅｅｉ＝０,Ｅｅ＾２ｉ＝σ＾２１。假设ｙ１,ｙ２,…,ｙｎ受到另一独立同分布随机变量序列μ１,μ,…,μｎ的污染,我们仅能观察到污染数据ｙ＾＊ｉ＝（１－ν）ｙｉ,ν为未知的污染参数。本文用污染数据给出了β的最小一乘估计,并证明了它的渐近正态性和相合性。     

非线性E-V回归模型中参数估计的渐近性质

本文讨论了非线性E-V回归模型中参数的估计问题,构造了未知参数β0的最小二乘估计β和误差方差σ2的估计σ2,证明了β具有渐近正态性,同时也证明了σ2依概率收敛于σ2的速度可达到n-1/2.     

非线性E-V回归模型中参数估计的渐近性质

本文讨论了非线性E-V回归模型中参数的估计问题,构造了未知参数β0的最小二乘估计β和误差方差σ2的估计σ2,证明了β具有渐近正态性,同时也证明了σ2依概率收敛于σ2的速度可达到n-1/2.     

污染数据回归分析中估计的强相合性

考虑简单回归模型（Ⅰ）yi＝α＋xiβ＋εi，i＝1，2，…，n，与半参数回归模型（Ⅱ）yi＝xiβ＋g（ti）－εi，i＝1，2，…，n，其中Eεi＝0，Eεi2＝σ12．假定y1，y2，…，yn受到另一独立同分布随机变量序列μ1，μ2，…，μn的污染，且仅能观察到污染数据，｛μi｝与｛yi｝独立．对文[1]，[2]中给出的α，β，g（·）及污染参数v的估计，本文在适当的条件下，证明了它们的强相合性．     

关于两类污染数据回归分析的参数估计

研究简单回归模型：ｙｊ＝ａ＋βｘｊ＋εｊ，ｊ＝１，２，…，ｎ，其中Ｅεｊ＝０，Ｅε＾２ｊ＝σ＾２ｊ；但ｙ１，ｙ２，…，ｙｎ受到另一独立同分布随机变量序列ｔ，ｔ２，…，ｔｎ两种不同方式的污染，ｔｊ与ｙｊ独立。本文给出了两种污染方式下的ａ、β和污染参数的估计。     

污染数据半参数回归模型中估计的相合性

利用最小二乘估计方法和权函数法给出了半参数模型Y=βX g(T) ε在某种污染方式下,,βg和污染系数的估计,并在适当条件下证明了它们具有相合性.     

纵向数据半参数回归模型的最小二乘局部线性估计

本文考虑纵向数据半参数回归模型:Yij=XiTjβ+g(Tij)+iεj,基于最小二乘法和局部线性拟合的方法建立了模型中参数分量β,回归函数g(.)和误差方差σ2的估计,在适当条件下给出了估计量的相合性,通过模拟研究说明了该方法在有限样本情况下具有良好的性质。     

纵向数据变系数回归模型小波估计的渐近性质

考虑纵向数据下的变系数回归模型y_(ij)=x_(ij)~Tθ(t_(ij))+e_(ij)i=1,2,…,n j=1,2,…,m.利用小波光滑和加权最小二乘方法,分别研究了模型中未知参数θ(·)的小波估计θ(·)和误差方差σ~2的小波估计σ~2,在适当的条件下,证明了θ的强相合性,强相合速度,并得到θ和σ~2的渐近正态性.     

半参数回归模型的估计的渐近性质

考虑半参数回归模型ｙｉ＝ｘｉ＾１β＋ｇ（ｔｉ）＋ｅｉ,１≤ｉ≤ｎ;其中ｇ为Ｒ上未知函数,σ０＾２＝Ｄ（ｅ１）柴根象等在１９９５年给出了β的二阶段估计βｎ,本文基于β１建立了σ０＾２的估计量σｎ＾２,研究了误差方差估计σｎ＾２的渐近正态性和强相合性,并且得到了可直接用于统计推断的统计量及其分布。     

固定设计下一类半参数回归模型的渐近性质

对固定设计下的一类半参数回归模型yi=xiβ+g(xi)+ei,i=1,2,…,n,综合最小二乘和非参数权函数估计方法,定义了,βg的估计量β∧n,gn∧及误差方差σ2的估计量σ2n∧.在适当条件下,证明它们具有强相合性和p(2)阶平均相合性.模拟的结果表明所得结果具有优良的性质.     

A contamination carrying criterion for branched surfaces

A contamination in a 3-manifold is an object interpolating between the contact structure and the lamination. Contaminations seem to provide a link between 3-dimensional contact geometry and the classical topology of 3-manifolds, as described in a separate paper (Oertel and Świa̧tkowski, Contact structures, σ-confoliations, and contaminations in 3-manifolds. arXiv math.GT/0307177). In this article we deal with contaminations carried by branched surfaces, giving a sufficient condition for a branched surface to carry a pure contamination.     

可压缩核废料污染问题的修正迎风差分格式及收敛性分析

对无弥散项的可压缩核废料污染问题模型建立修正迎风差分格式, 并进行了收敛性分析,最后给出其l^2误差估计.     

可压缩核废料污染问题的修正迎风差分格式及收敛性分析

对无弥散项的可压缩核废料污染问题模型建立修正迎风差分格式,并进行了收敛性分 析,最后给出其l2误差估计．     

Bayesian robustness under a skew-normal class of prior distribution

We develop a global sensitivity analysis to measure the robustness of the Bayesian estimators with respect to a class of prior distributions. This class arises when we consider multiplicative contamination of a base prior distribution. A similar structure was presented by van der Linde [12]. Some particular specifications for this multiplicative contamination class coincide with well known families of skewed distributions. In this paper, we explore the skew-normal multiplicative contamination class for the prior distribution of the location parameter of a normal model. Results of a Bayesian conjugation and expressions for some measures of distance between posterior means and posterior variance are obtained. We also elaborate on the behavior of the posterior means and of the posterior variances through a simulation study.     

含弥散可压核废料污染问题的交替方向特征有限元格式及分析

对可压缩并含弥散的核废料污染问题,利用有限元块逼近技术提出了交替方向特征有限元格式,格式兼具特征线逼近技术及交替方向技术的优点,证明了格式的最佳H^1-收敛阶。     

Testing the structure of multistage stochastic programs

A fixed topology of stages and/or a fixed branching scheme are common assumptions for applications and numerical solution of scenario based multistage stochastic programs. Using contamination technique to test this structure, we extend the results of Dupačová (Contamination for multistage stochastic programs. In: Hušková M, Janžura M (eds) Prague stochastics. Matfyzpress, Praha, pp 91–101, 2006a) to stochastic programs with multistage polyhedral risk objectives. The ideas are exemplified by bond portfolio management problems and complemented by illustrative numerical results.     

RFID-generated traceability for contaminated product recall in perishable food supply networks

As perishable food supply networks become more complex, incidents of contamination in these supply networks have become fairly common. Added to this complexity is the fact that there have been long delays in identifying the contamination source in several such incidents. Even when the contamination source was identified, there have been cases where the ultimate destination of all contaminated products were not known with complete certainty due, in part, to dispersion in these supply networks. We study the recall dynamics in a three-stage perishable food supply network through three different visibility levels in the presence of contamination. Specifically, we consider allocation of liability among the different players in the perishable supply network based on the accuracy with which the contamination source is identified. We illustrate the significance of finer levels of granularity both upstream and downstream as well as determine appropriate visibility levels and recall policies.     

Estimating copula densities, using model selection techniques

Recently a new way of modeling dependence has been introduced considering a sequence of parametric copula models, covering more and more dependency aspects and thus giving a closer approximation to the true copula density. The method uses contamination families based on Legendre polynomials. It has been shown that in general after a few steps accurate approximations are obtained. In this paper selection of the adequate number of steps is considered, and estimation of the unknown parameters within the chosen contamination family is established, thus obtaining an estimator of the unknown copula density. There should be a balance between the complexity of the model and the number of parameters to be estimated. High complexity gives a low model error, but a large stochastic or estimation error, while a very simple model gives a small stochastic error, but a large model error. Techniques from model selection are applied, thus letting the data tell us which aspects are important enough to capture into the model. Natural and simple estimators of the involved Fourier coefficients complete the procedure. Theoretical results show that the expected quadratic error is reduced by the selection rule to the same order of magnitude as in a classical parametric problem. The method is applied on a real data set, illustrating that the new method describes the data set very well: the error involved in the classical Gaussian copula density is reduced with no fewer than 50%.     

不可压核废料污染问题沿特征线的混合有限元方法

本文对具有分子弥散项的不可压核废料污染问题提出了沿特征方向的混合元离散格式，即对流动方程采用混合元格式，而对浓度方程和传热方程沿特征方向有限元离散。我们的论证表明：该格式具有最优收敛精度；时间步长△ｔ的前置常数仅依赖二阶方向导数；对△ｔ的限制由标准元方法中的△ｔ＝ｏ（ｈ）＾［２］减弱为△ｔ＝Ｏ（ｈ）。