一类有理分式的积分

本文介绍了一类有理分式的积分,并得到了一组织分公式。     

激光反射式光电水位测量仪

本文介绍一种以激光作为测量光源,利用光敏电阻随光线强弱变化而阻值随之变化的特性,控制或门电路工作,并在数字电压表上显示水面高度的测量仪。     

计算机病毒及其防治的探讨

本文阐述了计算机病毒的概念,对计算机病毒进行了分类,同时分析了各类病毒的特点。并着重介绍了对病毒的防范措施以及清除的方法。     

量子线中施主杂质的光致电离截面

本文就几种不同的线宽计算了准一维GaAs量子线中施主杂质的光致电离截面,同时也研究了磁场对光谱的影响。     

Conditional variance estimation in heteroscedastic regression models

First, we propose a new method for estimating the conditional variance in heteroscedasticity regression models. For heavy tailed innovations, this method is in general more efficient than either of the local linear and local likelihood estimators. Secondly, we apply a variance reduction technique to improve the inference for the conditional variance. The proposed methods are investigated through their asymptotic distributions and numerical performances.     

Partitioning k multivariate normal populations according to equivalence with respect to a standard vector

We propose optimal procedures to achieve the goal of partitioning k multivariate normal populations into two disjoint subsets with respect to a given standard vector. Definition of good or bad multivariate normal populations is given according to their Mahalanobis distances to a known standard vector as being small or large. Partitioning k multivariate normal populations is reduced to partitioning k non-central Chi-square or non-central F distributions with respect to the corresponding non-centrality parameters depending on whether the covariance matrices are known or unknown. The minimum required sample size for each population is determined to ensure that the probability of correct decision attains a certain level. An example is given to illustrate our procedures.     

收入分布函数在收入不平等研究领域的应用

收入分布函数的研究对于收入不平等的探讨意义重大,但是国内该领域的研究尚待进一步的拓展。笔者在文献回顾的基础上梳理了国外的相关研究,把常用的分布函数分成两参数分布和多参数分布函数两类,介绍了各类函数拟合居民实际收入分布的效果及其与基尼系数的关系;同时,本文剖析了各类函数之间的内在联系;进一步,基于分布函数的角度,笔者研究了收入流动性与收入不平等之间的联系,分析了平均数和中位数比值与基尼系数之间的数学关系以及如何利用该比值来估计分布函数的参数。利用这些研究结果,文章探讨了2010年我国城镇居民的收入结构和贫困问题并提出了一些前瞻性的研究建议。     

A Markov regime‐switching model for crude‐oil markets: Comparison of composite likelihood and full likelihood

We use the two‐state Markov regime‐switching model to explain the behaviour of the WTI crude‐oil spot prices from January 1986 to February 2012. We investigated the use of methods based on the composite likelihood and the full likelihood. We found that the composite‐likelihood approach can better capture the general structural changes in world oil prices. The two‐state Markov regime‐switching model based on the composite‐likelihood approach closely depicts the cycles of the two postulated states: fall and rise. These two states persist for on average 8 and 15 months, which matches the observed cycles during the period. According to the fitted model, drops in oil prices are more volatile than rises. We believe that this information can be useful for financial officers working in related areas. The model based on the full‐likelihood approach was less satisfactory. We attribute its failure to the fact that the two‐state Markov regime‐switching model is too rigid and overly simplistic. In comparison, the composite likelihood requires only that the model correctly specifies the joint distribution of two adjacent price changes. Thus, model violations in other areas do not invalidate the results. The Canadian Journal of Statistics 41: 353–367; 2013 © 2013 Statistical Society of Canada     

Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces

In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problems, and the magnitude of their effects tapers off. It is reasonable to model the number of relevant features as a diverging sequence when sample size increases. In this paper, we investigate the properties of the extended Bayes information criterion (EBIC) (Chen and Chen, 2008) for feature selection in linear regression models with diverging number of relevant features in high or ultra-high dimensional feature spaces. The selection consistency of the EBIC in this situation is established. The application of EBIC to feature selection is considered in a SCAD cum EBIC procedure. Simulation studies are conducted to demonstrate the performance of the SCAD cum EBIC procedure in finite sample cases.