改进核函数的支持向量机智能诊断方法研究

针对目前支持向量机( SVM)智能诊断方法核函数选择困难以及参数选择具有随意性的问题,提出了基于模拟退 火算法改进核函数的SVM智能诊断方法,重新设计了支持向量机的核函数以及参数。多项式核函数是局部核函数具有 较强的拟合能力,而径向基核函数是全局核函数具有较强的外推能力,根据Mercer理论,建立一种由多项式核函数与径 向基核函数组合而成的复合核函数,并利用模拟退火算法全局寻优的优点,对支持向量机的参数做最优选择;改进后的 SVM运用于轴承故障诊断。研究结果表明：相对于传统SVM法,该方法具有较好的学习效率及较高的诊断准确率;该方 法运用于轴承故障诊断领域极大地提高了故障诊断的准确率以及诊断效率。该研究为基于模拟退火算法改进核函数的 SVM智能诊断方法应用于机械设备故障诊断提供了相应的理论和实践依据。     

基于GARCH模型和非参数模型的上海股市VaR的比较

从VaR的定义出发,考虑基于GARCH类模型和基于非参数模型的VaR估计.对于前者进行筛选以后,采用EGARCH(1,1)模型来估计资产或资产组合未来收益率的波动,后者则用核估计方法估计资产或资产组合未来收益率的概率密度函数,并用这两个模型对上海股市进行了实证研究,结果表明后者不失为一种值得考虑的方法.     

城镇化、水资源消耗的动态演进与门槛效应

基于中国30个省市1998—2015年面板数据,综合利用核密度估计函数、非动态面板门槛回归模型系统考察城镇化与水资源消耗的动态演进过程和非线性关系。研究结果表明:（1）全国城镇化率呈两极分化态势,东部、中部、西部三大区域间城镇化受经济发展等因素影响而存在差异;全国水资源消耗得到控制,但东部、中部、西部水资源消耗高的省份存在增加趋势。（2）全国城镇化对水资源消耗影响存在显著的经济发展和居民收入门槛,并具有显著的抑制效应;东部经济发展门槛具有抑制效应,中部居民收入门槛具有促进效应;西部门槛效应与全国相同,且经济发展抑制作用阶段性增强。（3）居民收入、资本存量、人力资本与水资源消耗正相关,而经济发展与之负相关。     

Bayesian marginal inference via candidate's formula

Computing marginal probabilities is an important and fundamental issue in Bayesian inference. We present a simple method which arises from a likelihood identity for computation. The likelihood identity, called Candidate's formula, sets the marginal probability as a ratio of the prior likelihood to the posterior density. Based on Markov chain Monte Carlo output simulated from the posterior distribution, a nonparametric kernel estimate is used to estimate the posterior density contained in that ratio. This derived nonparametric Candidate's estimate requires only one evaluation of the posterior density estimate at a point. The optimal point for such evaluation can be chosen to minimize the expected mean square relative error. The results show that the best point is not necessarily the posterior mode, but rather a point compromising between high density and low Hessian. For high dimensional problems, we introduce a variance reduction approach to ease the tension caused by data sparseness. A simulation study is presented.     

论中国国家安全利益及国家安全利益区

本文首先介绍了国家安全利益的概念,阐述了我国国家安全利益的基本涵义;接着分析国家安全利益区的性质特征,提出了确定国家安全利益区的基本原则和一般方法;最后,阐明了中国的国家安全利益区,着重剖析了我国安全利益区的核心层和内层。     

A New Kernel Distribution Function Estimator Based on a Non-parametric Transformation of the Data

Abstract.  A new kernel distribution function (df) estimator based on a non-parametric transformation of the data is proposed. It is shown that the asymptotic bias and mean squared error of the estimator are considerably smaller than that of the standard kernel df estimator. For the practical implementation of the new estimator a data-based choice of the bandwidth is proposed. Two possible areas of application are the non-parametric smoothed bootstrap and survival analysis. In the latter case new estimators for the survival function and the mean residual life function are derived.     

Improved distribution quantile estimation

A technique for estimating the quantiles or percentiles of a distribution is developed. The parametric form of the distribution is assumed unknown. The estimation procedure is based on a kernel estimator of a probability density function and on aquantile estimator suggested by Harrell and Davis (1982). Simulation studies show that estimation of quantiles in moderately heavyto heavy tails of a distribution is substantially improved by use of the technique.     

Nonlinear regression in a trivariate elliptical distribution via a linear combination of order statistics

In this paper, by assuming that (X, Y ₁, Y ₂)^T has a trivariate elliptical distribution, we derive the exact joint distribution of X and a linear combination of order statistics from (Y ₁, Y ₂)^T and show that it is a mixture of unified bivariate skew-elliptical distributions. We then derive the corresponding marginal and conditional distributions for the special case of t kernel. We also present these results for an exchangeable case with t kernel and illustrate the established results with an air-pollution data.     

Smooth nonparametric estimation of thedistribution and density functions from record-breaking data

In some experiments, such as destructive stress testing and industrial quality control experiments, only values smaller than all previous ones are observed. Here, for such record-breaking data, kernel estimation of the cumulative distribution function and smooth density estimation is considered. For a single record-breaking sample, consistent estimation is not possible, and replication is required for global results. For m independent record-breaking samples, the proposed distribution function and density estimators are shown to be strongly consistent and asymptotically normal as m → ∞. Also, for small m, the mean squared errors and biases of the estimators and their smoothing parameters are investigated through computer simulations.     

Weak and strong uniform consistency rates of kernel density estimates for randomly censored data

Let X₁,., X_n, be i.i.d. random variables with distribution function F, and let Y₁,.,.,Y_n be i.i.d. with distribution function G. For i = 1, 2,.,., n set δ_i, = 1 if X_i ≤ Y_i, and 0 otherwise, and X_i, = min{X_i, K_i}. A kernel-type density estimate of f, the density function of F w.r.t. Lebesgue measure on the Borel o-field, based on the censored data (δ_i, X_i), i = 1,.,.,n, is considered. Weak and strong uniform consistency properties over the whole real line are studied. Rates of convergence results are established under higher-order differentiability assumption on f. A procedure for relaxing such assumptions is also proposed.