Semiparametric estimation of the long-range parameter

We study two estimators of the long-range parameter of a covariance stationary linear process. We show that one of the estimators achieve the optimal semiparametric rate of convergence, whereas the other has a rate of convergence as close as desired to the optimal rate. Moreover, we show that the estimators are asymptotically normal with a variance, which does not depend on any unknown parameter, smaller than others suggested in the literature. Finally, a small Monte Carlo study is included to illustrate the finite sample relative performance of our estimators compared to other suggested semiparametric estimators. More specifically, the Monte-Carlo experiment shows the superiority of the proposed estimators in terms of the Mean Squared Error. The first author research was funded by the Economic and Social Research Council (ESRC) reference number: R000238212. The second author research was funded by the Ministry of Education, Culture, Sports and Technology of Japan, reference number: 09CE2002 and B(2)10202202.     

中国银行间拆借利率扩散模型的极大拟似然估计

本文用极大拟似然估计法估计了中国银行间市场七天拆借利率扩散模型的参数。并用自助法对众多不同的模型进行了广义拟似然比检验。结论表明:中国货币市场利率具有均值回复效应:利率敏感系数γ值为1.421265,对利率水平具有较高敏感性。     

H1弱拓扑中GBBM方程整体吸引子的存在性

文章讨论无界区域上GBBM方程的Cauchy问题,对方程的解进行了先验估计,并证明了在H1弱拓扑中整体吸引子的存在性.     

Clustering Effect on the Statistical Estimation Accuracy of Distribution Density

The paper is devoted to statistical nonparametric estimation of multivariate distribution density. The influence of data pre-clustering on the estimation accuracy of multimodal density is analyzed by means of the Monte Carlo method. It is shown that the soft clustering is more advantageous than the hard one. While a moderate increase in the number of clusters also increases the calculation time, it considerably reduces the estimation error.     

Regularization of some linear ill-posed problems with discretized random noisy data

For linear statistical ill-posed problems in Hilbert spaces we introduce an adaptive procedure to recover the unknown solution from indirect discrete and noisy data. This procedure is shown to be order optimal for a large class of problems. Smoothness of the solution is measured in terms of general source conditions. The concept of operator monotone functions turns out to be an important tool for the analysis.

     

中高温工质在蒸发器中的换热模拟分析

本文采用分布参数的方法,建立了水源热泵中普遍采用的壳管式蒸发器稳态数学模型.通过中高温水源热泵试验台的实验数据同模拟计算结果的对比,验证了数学模型的准确性。应用此数学模型对中高温工质HTR01、HTR02及R134a在蒸发器典型制热运行工况,进行了模拟计算。得出了三种工质制冷剂侧换热系数沿管长的变化关系并对三种工质的换热性能进行了对比分析,为采用中高温工质的水源热泵换热器设计提供了参考依据.     

离化团簇束法制备c轴取向的ZnO薄膜

为了探索低温可调控ZnO薄膜沉积技术,提出了一种新的ZnO薄膜制备方法,即离化团簇束（ICB）法,并自行设计研制了应用该方法制备ZnO薄膜的专门装置.采用超音速喷嘴获得高速锌原子团簇束,用Hall等离子体源产生氧离子束离化锌原子团簇,获得了较高的离化率.在沉积过程中,可以通过调节衬底偏压、氩氧比、衬底加热温度等参数,来控制成膜的质量;应用这个装置成功地在硅衬底上制备的ZnO薄膜,经XRD和EDS检测,薄膜的c轴取向一致,Zn、O原子百分比接近于1：1,成膜质量好.     

激光介质的多次吸收不均匀内热源模型

针对激光介质的内热源,以多次吸收作用为基础提出了适用于侧面双向泵浦板状激光介质的不均匀内热源模型,即多次吸收不均匀内热源模型,并与均匀内热源和一次吸收内热源模型进行了对比分析。研究表明,介质厚度及光吸收系数组成的无量纲参数不同时,三种内热源模型得到的内热源分布也有所不同。最后给出了内热源不均匀度及多次吸收模型与一次吸收模型之间的差异在不同目标值下的内热源模型适用准则。     

Dynamic Nonlinear Modelization of Operational Supply Chain Systems

Supply Chain Management (SCM) is an important activity in all producing facilities and in many organizations to enable vendors, manufacturers and suppliers to interact gainfully and plan optimally their flow of goods and services. To realize this, a dynamic modelling approach for characterizing supply chain activities is opportune, so as to plan efficiently the set of activities over a distributed network in a formal and scientific way. The dynamical system will result so complex that it is not generally possible to specify the functional forms and the parameters of interest, relating outputs to inputs, states and stochastic terms by experiential specification methods. Thus the algorithm that will presented is Data Driven, determining simultaneously the functional forms, the parameters and the optimal control policy from the data available for the supply chain. The aim of this paper is to present this methodology, by considering dynamical aspects of the system, the presence of nonlinear relationships and unbiased estimation procedures to quantify these relations, leading to a nonlinear and stochastic dynamical system representation of the SCM problem. Moreover, the convergence of the algorithm will be proved and the satisfaction of the required statistical conditions demonstrated. Thus SCM problems may be formulated as formal scientific procedures, with well defined algorithms and a precise calculation sequence to determine the best alternative to enact. A “Certainty equivalent principle” will be indicated to ensure that the effects of the inevitable uncertainties will not lead to indeterminate results, allowing the formulation of demonstrably asymptotically optimal management plans.     

Sequential nonlinear estimation with nonaugmented priors

How can the basic compatibility of theory and observations be investigated for nonlinear processes without requiring stochastic characterizations for residual error terms? The present paper proposes a flexible least-cost approach. For each possible estimatex for the sequence of process states, letc _D (x) andx _M(x) denote the costs incurred for deviations away from the prior dynamic specifications and prior measurement specifications, respectively. Define the cost-efficiency frontier to be the greatest lower bound for the set of all possible cost pairs [c _D (x),c _M(x)], conditional on the given observations. State sequence estimatesx that attain the cost-efficiency frontier indicate the possible ways that the actual process could have developed over time in a manner minimally incompatible with the prior dynamic and measurement specifications. An algorithm is developed for the exact sequential updating of the cost-efficient state sequence estimates as the duration of the process increases and additional observations are obtained.