Catalogue of small runs nonregular designs from hadamard matrices with generalized minimum aberration

Fries and Hunter ( 1980 ) proposed the Minimum Aberration criterion (MA) for selecting regular designs. The regular designs with MA are msot commonly used because they are considered as the best designs. How ever, as pointed out by Chen, Sun and Wu ( 1993 ), there are situations that other designs may better meet the design need. Therefore, they catalogued some two-level and three-level fractional factorial regular designs with small (16,27,32,64) runs. For nonregular designs, such as the ones taken from Hadamard matrices, the MA criterion is not appUcable. Deng and Tang ( 1999 ) introduced Generalized Minimum Aberration Criterion (GMA) as a natural extension to the MA criterion. Similar to the case in the regular designs, other designs may better meet practical need, In this paper, we use the GMA criterion to give a catalogue of nonregular designs with smaU (16,20,24) runs.     

Maximum likelihood estimation for the beta distribution

A fast method of calculating the two-parameter maximum-likelihood estimates of the beta distribution is given which does not require starting values and is generally free from convergence problems.     

Dominating james-stein positive-part estimator for normal mean with unknown covariance matrix

Assume that we have a random sample of size n from p-variate normal population and we wish to estimate the mean vector under quadratic loss with respect to the inverse of the unknown covariance matrix, A class of superior estimators to James-Stein positive part estimator is given when n>max{9p+10,13p-7}, based on the argument by Shao and Strawderman(1994).     

Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model

We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model, based on the Gaussian likelihood conditional on initial values. We give conditions on the parameters such that the process X_t is fractional of order d and cofractional of order d−b; that is, there exist vectors β for which β^′X_t is fractional of order d−b and no other fractionality order is possible. For b=1, the model nests the I(d−1) vector autoregressive model. We define the statistical model by 0 < bd, but conduct inference when the true values satisfy 0d₀−b₀<1/2 and b₀≠1/2, for which β₀^′X_t is (asymptotically) a stationary process. Our main technical contribution is the proof of consistency of the maximum likelihood estimators. To this end, we prove weak convergence of the conditional likelihood as a continuous stochastic process in the parameters when errors are independent and identically distributed with suitable moment conditions and initial values are bounded. Because the limit is deterministic, this implies uniform convergence in probability of the conditional likelihood function. If the true value b₀>1/2, we prove that the limit distribution of is mixed Gaussian, while for the remaining parameters it is Gaussian. The limit distribution of the likelihood ratio test for cointegration rank is a functional of fractional Brownian motion of type II. If b₀<1/2, all limit distributions are Gaussian or chi‐squared. We derive similar results for the model with d = b, allowing for a constant term.     

基于支持向量机的人的全面发展评价模型及省份实证

根据“坚持以人为本,树立全面、协调、可持续的发展现,促进经济社会和人的全面发展”的科学发展观的内涵,通过人的全面发展评价指标的海选、筛选和理性分析构建了人的全面发展综合评价指标体系.建立了基于支持向量机(Support Vector Machines,SVM)的人的全面发展评价模型,并对我国2006年典型的14个省级行政区人的全面发展状况进行实证分析.本文的创新与特色一是通过高斯核支持向量机把评价指标空间映射到高维特征空间,解决了人的全面发展评价影响因素非线性赋权问题.克服了现有评价方法均采用线性加权方式计算评价结果、不能表达指标与评价结果间的真实关系的缺陷.二是通过正交设计确定支持向量机训练样本的输入数据,并通过AHP确定训练样本的权重,得到指标训练样本的评价结果作为训练样本的输出.解决了在没有训练样本输入和输出情况下如何确定指标训练样本的问题.这就解决了在缺乏训练样本输入、输出数据情况下如何应用支持向量机进行回归,以得到评价结果的问题.三是通过人口累积比重、收入累积比重、通货膨胀等可获得数据指标计算准基尼系数和准国民幸福指数,解决了基尼系数和国民幸福指数的间接测算问题,进而解决了现阶段因统计数据缺失、而无法进行省级行政区人的全面发展评价的问题.     

财政政策动态效应与价格水平的财政决定理论检验

运用一个5变量的结构向量误差修正模型(SVECM),估计财政支出和财政收入外生冲击对GDP、价格水平等相关宏观经济变量的动态效应,同时对价格水平的财政决定理论(FTPL)进行检验。冲击的识别结合相关财政政策决策滞后的假定采用区分持久冲击和暂时冲击的方法。结果表明,正的政府收入冲击将显著减少产出并降低价格水平;正的政府支出冲击对产出的正面影响不如减少财政收入的冲击,其对价格水平的影响也不显著。有理由相信,减税比扩大政府支出更有利于经济增长。另外,在选取的样本期内,价格水平的财政决定理论得不到实证支持。     

欧洲二氧化碳期货市场有效性分析

欧盟排放贸易体系(EU ETS)是主要的国际碳交易市场,该体系排放许可单位(EUAs)的交易市场发展迅猛。为了帮助市场参与者更好地规避市场价格风险,利用单位根检验和协整关系检验验证了欧洲二氧化碳期货市场的有效范围,发现其在1个月内是有效的。在市场有效性范围内,对现货价格和期货价格建立了向量误差修正模型,同时发现该期货市场某时刻价格的影响可以持续3个月。     

Prediction of bankruptcy using support vector machines: an application to bank bankruptcy

The purpose of this study was to apply support vector machines (SVMs) to bank bankruptcy analysis using practical steps. Although the prediction of the financial distress of companies is done using several statistical and machine learning techniques, bank classification and bankruptcy prediction still need to be investigated because few investigations have been conducted in this field of banking. In this study, SVMs were implemented to analyse financial ratios. Data sets from Turkish commercial banks were used. This study shows that SVMs with the Gaussian kernel are capable of extracting useful information from financial data and can be used as part of an early warning system.     

An Optimal Experimental Design Perspective on Radial Basis Function Regression

This article provides a new look at radial basis function regression that reveals striking similarities with the traditional optimal experimental design framework. We show theoretically and computationally that the so-called relevant vectors derived through the relevance vector machine (RVM) and corresponding to the centers of the radial basis function network, are very similar and often identical to the support points obtained through various optimal experimental design criteria like D-optimality. This allows us to provide a statistical meaning to the relevant centers in the context of radial basis function regression, but also opens the door to a variety of ways of approach optimal experimental design in multivariate settings.     

Combining Multi-class SVMs with Linear Ensemble Methods that Estimate the Class Posterior Probabilities

Roughly speaking, there is one main model of pattern recognition support vector machine, with several variants of lower popularity. On the contrary, among the different multi-class support vector machines which can be found in the literature, none is clearly favoured. On the one hand, they exhibit distinct statistical properties. On the other hand, multiple comparative studies between multi-class support vector machines and decomposition methods have highlighted the fact that each model has its advantages and drawbacks. These observations call for the evaluation of combinations of multi-class support vector machines. In this article, we study the combination of multi-class support vector machines with linear ensemble methods. Their sample complexity is low, which should prevent them from overfitting, and the outputs of two of them are estimates of the class posterior probabilities.