Multivariate Liouville distributions

A random vector (X₁, …, X_n), with positive components, has a Liouville distribution if its joint probability density function is of the formf(x₁ + … + x_n)x₁^a₁.1 … x_n^a_n.1 with thea_i all positive. Examples of these are the Dirichlet and inverted Dirichlet distributions. In this paper, a comprehensive treatment of the Liouville distributions is provided. The results pertain to stochastic representations, transformation properties, complete neutrality, marginal and conditional distributions, regression functions, and total positivity and reverse rule properties. Further, these topics are utilized in various characterizations of the Dirichlet and inverted Dirichlet distributions. Matrix analogs of the Liouville distributions are also treated, and many of the results obtained in the vector setting are extended appropriately.     

不等式约束条件下回归模型的参数估计及检验

本文研究了具有不等式约束条件的回归模型的参数估计，给出了检验的统计量和一个实例．     

Panel‐based stratified cluster sampling and analysis for photovoltaic outdoor measurements

We study a stratified multisite cluster‐sampling panel time series approach in order to analyse and evaluate the quality and reliability of produced items, motivated by the problem to sample and analyse multisite outdoor measurements from photovoltaic systems. The specific stratified sampling in spatial clusters reduces sampling costs and allows for heterogeneity as well as for the analysis of spatial correlations due to defects and damages that tend to occur in clusters. The analysis is based on weighted least squares using data‐dependent weights. We show that this does not affect consistency and asymptotic normality of the least squares estimator under the proposed sampling design under general conditions. The estimation of the relevant variance–covariance matrices is discussed in detail for various models including nested designs and random effects. The strata corresponding to damages or manufacturers are modelled via a quality feature by means of a threshold approach. The analysis of outdoor electroluminescence images shows that spatial correlations and local clusters may arise in such photovoltaic data. Further, relevant statistics such as the mean pixel intensity cannot be assumed to follow a Gaussian law. We investigate the proposed inferential tools in detail by simulations in order to assess the influence of spatial cluster correlations and serial correlations on the test's size and power. ©2016 The Authors. Applied Stochastic Models in Business and Industry published by John Wiley & Sons, Ltd.     

基于投影算子的正则化最小二乘回归

通过引入经验覆盖数（empirical covering number）和投影算子（projection-operator）,从理论上研究正则化最小二乘回归学习算法.与已有的方法相比,一方面简化了回归分析的过程;另一方面,提高了最小二则回归学习算法的误差收敛阶.即,通过引入投影算子,得到了O（m-1）型的收敛阶,这是统计学习理论中关于泛化误差的最佳逼近阶.     

中国出境旅游的外交效应

2000年以来,中国出境旅游高增长、高消费,影响力不断增大,成为国家外交战略的重要内容,外交效应逐渐显现。通过辨析中国出境旅游外交效应的概念、表现形式和结果,基于10个中国主要出境目的国的旅游互动数据,采用DIF-GMM计量经济模型,实证检验了中国出境旅游外交效应。结果表明,中国通过有序推进ADS协议、加强经济援助、举办“旅游年”活动、实施旅游“制裁”,并积极参与国际制度建设等旅游外交行为,促进了与世界各国的友好交往,维护了自身核心利益,提升了外交软实力和国际影响力。国际旅游反作用于国际关系,对国际关系具有显著的正向促进作用,不仅是国际关系的结果,而且是其重要动因之一。中国出境旅游与政治、经济、社会、文化等联动,多方面提升了中国的国际影响力。     

应用线性回归方法对无形资产进行收益预测

无形资产评估对引导列形资产的建立和完善具有重要作用，收益法是常用的无形资产评估方法，而收益的预测是无形资产评估中的一个难题，本文以新疆某上市公司为例，尝试在无形资产评估方法中用多元线性回归进行收益预测。     

宁波市耕地动态及其驱动力的初步研究

利用宁波市55年长序列耕地统计数据,20余年相关社会经济资料,从宁渡市耕地的数量变化、利用程度的变化和变化的区域差异等方面,分析了宁波市耕地动态变化的趋势和规律．应用主成分分析,定量地诊断出耕地变化的驱动因子,揭示耕地变化的驱动机制．结果表明：耕地数量总体呈不断减少趋势,人口压力、经济发展动态是影响宁波市耕地动态变化的主要动因．在此基础上,运用多元回归分析建立耕地变化回归模型．该成果将为宁波市合理地利用和保护耕地提供理论指导．     

线性模型中相依误差下回归系数最小二乘估计的相合性

本文研究了线性模型y_i=X_i’β+e_i,i=1,2,…中回归系数β=(β_1,…,β_n)’的最小二乘估计的相合性.文章前一部分考虑了随机误差序列{e_i}相依情况下的(?)的γ-阶平均相合性,改进了胡舒合在[1]中的有关结果.文章后一部分推广陈希孺在[2]中的(?)的强相合性于相依情形.     

When is the pseudo-best estimator blue?

Summary Linear unbiased estimation of the mean of a random variable in Hilbert space is treated in the typical case where the mean belongs to a known subspace. The best linear estimate depends on the underlying covariance operatorB ₀ of the random variable. This operatorB ₀, however, is rarely completely known, so that an auxiliary operatorB is used to compute a “pseudo-best” estimate. It is shown that the best and the pseudo-best estimates coincide, if and only ifB ₀ B ⁻¹ leavesM invariant. Applications to linear regression are to be found in the references.     

带优化参数的SVR模型及在软件成本估算中的应用

随着社会各行各业对软件开发投资的日益增长,产业界和学术界越来越关注可靠的软件成本估算,以有效控制软件开发过程中相关风险.为了能更准确地估算软件成本,提出一种带遗传算法优化参数的支持向量回归机模型,用遗传算法来优化支持向量回归机模型中的参数集(C,γ,ε),可以避免参数选择的盲目性,能显著提高支持向量回归机模型的预测能力.分别用IBM DP、Kemerer和Hallmark三个数据库来验证模型的有效性,并与常用的线性回归模型进行对比,结果显示采用遗传算法优化的支持向量回归机模型具有很好的学习精度和推广能力,在MMRE和Pred(0.25)两个标准上都优于线性回归模型.