电信运营企业节能指标分解模型研究

从单位业务耗电量偏离度和节电量占比两个维度建立节能潜力-意愿矩阵,利用主成分分析和分层聚类分析将分公司按照节能潜力和意愿的高低细分为4类。提出结合非参数检验、回归分析、单位业务耗电量分解和节电量分解方法的节能指标分解模型。通过分析计算将某省电信运营企业下辖21个分公司分类,并提出不同类别的分公司的节能指标。     

Reducing data dimension for cluster detection

Clustering high-dimensional data is often a challenging task both because of the computational burden required to run any technique, and because the difficulty in interpreting clusters generally increases with the data dimension. In this work, a method for finding low-dimensional representations of high-dimensional data is discussed, specifically conceived to preserve possible clusters in data. It is based on the critical bandwidth, a nonparametric statistic to test unimodality, related to kernel density estimation. Some useful properties of the aforementioned statistic are enlightened and an adjustment to use it as a basis for reducing dimensionality is suggested. The method is illustrated by simulated and real data examples.     

Testing the Equality of Covariance Operators in Functional Samples

Abstract. We propose a non‐parametric test for the equality of the covariance structures in two functional samples. The test statistic has a chi‐square asymptotic distribution with a known number of degrees of freedom, which depends on the level of dimension reduction needed to represent the data. Detailed analysis of the asymptotic properties is developed. Finite sample perfo‐rmance is examined by a simulation study and an application to egg‐laying curves of fruit flies.     

Strategies for Fitting Large,Geostatistical Data in MCMC Simulation

Models for geostatistical data introduce spatial dependence in the covariance matrix of location-specific random effects. This is usually defined to be a parametric function of the distances between locations. Bayesian formulations of such models overcome asymptotic inference and estimation problems involved in maximum likelihood-based approaches and can be fitted using Markov chain Monte Carlo (MCMC) simulation. The MCMC implementation, however, requires repeated inversions of the covariance matrix which makes the problem computationally intensive, especially for large number of locations. In the present work, we propose to convert the spatial covariance matrix to a sparse matrix and compare a number of numerical algorithms especially suited within the MCMC framework in order to accelerate large matrix inversion. The algorithms are assessed empirically on simulated datasets of different size and sparsity. We conclude that the band solver applied after ordering the distance matrix reduces the computational time in inverting covariance matrices substantially.     

Local Convergence Rate of Mean Squared Error in Density Estimation

The local convergence rate of a multivariate density estimators based on the certain delta-sequence is studied. In contrast to known results, the conditions on the density are formulated in terms of the modulus of continuity. The main contribution of this study is relaxing the corresponding smoothing conditions in terms of arbitrary modulus of continuity type majorant. In particular, when the density f ∈ L ^p (R ^d ) satisfies Lipschitz condition of order γ = 1 at x, the rate of convergency contains terms with logarithm, which is the best possible convergency rate.     

Geometric programming for optimal allocation of integrated samples in quality control

We apply geometric programming, developed by Duffin, Peterson Zener (1967), to the optimal allocation of stratified samples. As an introduction, we show how geometric programming is used to allocate samples according to Neyman (1934), using the data of Cornell (1947) and following the exposition of Cochran (1953).

Then we use geometric programming to allocate an integrated sample introduced by Schwartz (1978) for more efficient sampling of three U. S. Federal welfare quality control systems, Aid to Families with Dependent Children, Food Stamps and Medicaid.

We develop methods for setting up the allocation problem, interpreting it as a geometric programming primal problem, transforming it to the corresponding dual problem, solving that, and finding the sample sizes required in the allocation problem. We show that the integrated sample saves sampling costs.     

基于前景理论的核电厂作业行为监管演化博弈研究

为提高对核电厂作业行为安全监管的效果,减少运行事件的发生概率,文章考虑参与主体的风险偏好,基于前景理论构建核安全监管机构、操纵员和核电营运单位三方的作业行为监管演化博弈模型,并对影响因素进行模拟仿真。结果表明,监管机构和核电营运单位的风险敏感系数的减小,会使得三方博弈最终不能稳定在最优策略上,而操纵员的价值感知系数使得其策略演化速度减慢,但并不影响最终策略选择。     

q－Hermite多项式与Askey－Wilson积分

应用ｑ－微分算子研究了ｑ－Ｈｅｒｍｉｔｅ多项式，得到了若干结论．应用这些结论简洁地计算了Ａｓｋｅｙ－Ｗｉｌｓｏｎ积分．     

Sikkema-Bézier型算子对有界变差函数的点态逼近度

构造了两种Sikkema -B啨ｚｉｅｒ型算子Ｓｎ,a(f,x) ,S n ,a(f,x) (它们是Sikkema算子的两种推广 ) ,并研究了它们对有界变差函数的点态逼近 ,得到它们对这类函数点态逼近的最优估计式     

美国英语的异质性及文化同质

美国英语是该国的社会和文化发展的产物,在许多方面都有别于英国英语。语言是文化的载体符号,它既反映文化又影响文化,同时也离不开文化的支撑。美国英语的变异程度很大,混杂的美国文化在美国英语的引导下,逐渐走向统一,凸显了文化的同质性。