Retail default prediction by using sequential minimal optimization technique

This paper employed sequential minimal optimization (SMO) to develop default prediction model in the US retail market. Principal components analysis is used for variable reduction purposes. Four standard credit scoring techniques—naïve Bayes, logistic regression, recursive partitioning and artificial neural network—are compared to SMO, using a sample of 195 healthy firms and 51 distressed firms over five time periods between 1994 and 2002. The five techniques perform well in predicting default particularly one year before financial distress. Furthermore, the prediction still remains sound even 5 years before default. No single methodology has the absolute best classification ability, as the model performance varies in terms of different time periods and variable groups. External influences have greater impacts on the naïve Bayes than other techniques. In terms of similarity with Moody's ranking, SMO excelled over other techniques in most of the time periods. Copyright © 2008 John Wiley & Sons, Ltd.     

Maxwell's color statistics: From reduction of visible errors to reduction to invisible molecules

This paper presents a cross-disciplinary and multi-disciplinary account of Maxwell's introduction of statistical models of molecules for the composition of gases. The account focuses on Maxwell's deployment of statistical models of data in his contemporaneous color researches as established in Cambridge mathematical physics, especially by Maxwell's seniors and mentors. The paper also argues that the cross-disciplinary, or cross-domain, transfer of resources from the natural and social sciences took place in both directions and relied on the complex intra-disciplinary, or intra-domain, dynamics of Maxwell's researches in natural sciences, in color theory, physical astronomy, electromagnetism and dynamical theory of gases, as well as involving a variety of types of communicating and mediating media, from material objects to concepts, techniques and institutions.     

列联表检验在疾病成因中的应用

在医学中,对某些疾病成因的探究越来越重要,鉴于其样本是离散多元的,列联表在医学,生物学中应用更加广泛。疾病及其成因的研究属于对分类变量进行的分析研究,利用列联表统计样本频数,利用假设检验中的卡方检验并参照临界值表,由此能判断分类变量之间是相关还是相互独立。利用交叉列联表探究现象与其因素的相关关系,而SPSS中的三种检验方法,即χ2检验、ψ系数、列联系数,充分的分析研究了统计量的相关关系,这对于疾病的预防及治疗有很大的帮助。     

基于时间序列的灰色预测技术在估产模型中的应用

在建立估产模型过程中，引进基于时间序列的灰色预测技术，通过对样本点建立基于时间序列的灰色预测模型和常规的多元线性回归气象模型的分析比较，试图找到一种计算简单，数据要求少而精度较高，时效性较好的建模方法，为时间序列预测在农作物估产方面的应用作出一点探索。     

基于分数低阶统计量的正交小波盲均衡算法

对于服从分数低阶Alpha稳定分布的非高斯信号,其二阶和高阶统计量都是不存在的。当环境噪声为这种噪声时,基于高阶统计量的常数模盲均衡算法(CMA)的均衡性能很差。为了克服环境噪声服从分数低阶Alpha稳定分布时,CMA的性能缺陷,提出了一种基于分数低阶统计量的正交小波盲均衡算法。该算法利用分数低阶统计量来抑制Alpha稳定噪声,根据最小分散系数准则优化盲均衡算法的权向量,并对均衡器输入信号进行正交小波变换,通过降低均衡器输入信号的自相关性来加快收敛速度。水声信道仿真结果表明,该算法性能明显优于CMA。     

晋城煤业集团科技创新能力分析

利用科技统计对晋城煤业集团在科技创新中的优势和劣势进行了分析，并进一步提出了对策及改进措施。     

小学语文教材用字的统计分析与问题讨论——以上海二期课改语文教材为例

通过对上海市小学语文教材用字的全面统计后发现,不论是用字的总量还是用字的字种数都存在问题:用字的总量太少,用字的字种数太多.这说明教材编写与课程标准的制定都还需要进一步的研究,都需要提高其操作的科学性.     

基于统计的智能输入技术分析

首先分析了智能输入技术的现状，介绍了基于统计的技术的实质，并分析了汉语言环境中运用Marcov模型实现智能输入遇到的一些具体细节问题，同时提出了针对这些问题的一些解决方法。