混合缺失机制下两总体差异的半经验似然置信区间

假定两个总体x与y均有数据缺失,它们的分布函数分别为F(·)与G_θ(·),其中F(·)未知,G_θ(·)的概率密度函数g_θ(·)形式已知,仅依赖于一些未知的参数,利用Fractional填补法填补缺失值,在一定的条件下证明了缺失数据下两总体差异指标的半经验似然比统计量的渐近分布为x_1~2,由此可构造两总体差异指标的经验似然置信区间.     

基于支持向量机的凝血功能诊断模型

使用凝血四项指标诊断凝血功能是临床的常规检查,但根据经验进行诊断正确率不高.剔除临床上最重要的指标FIB后,建立支持向量机模型诊断的正确率和根据凝血四项指标诊断的正确率没有显著差异,100次模拟的平均正确率分别达到了95.4496%和95.5039%.     

基于信息显著性差异的绿色产业评价指标筛选模型及实证

依据广义的绿色产业内涵,通过信息显著性差异与信息冗余分析定量筛选指标,建立了包括绿色生产指标层、绿色生态环境指标层、绿色消费方式指标层的大连市绿色产业评价指标体系。本文的创新与特色一是通过负载系数大小差异进行信息显著性分析,删除负载系数小、对综合评价结果影响弱的指标。克服了现有研究先进行相关分析剔除冗余指标,再利用负载系数大小差异筛选出反映信息显著性大的指标的弊端。事实上,指标间较高的相关性是计算负载系数的必要前提。因此,利用负载系数筛选指标前是不可进行指标的相关分析降低指标间的相关性的。二是通过结合专家知识经验辨析指标的实际含义,仅对反映信息可能重复的指标使用相关分析,剔除同指标层内相关系数较大的两个指标中反映指标层信息能力相对较弱的指标。克服了仅仅通过相关系数不能准确衡量指标间信息重复程度的弊端。研究结果表明,最终建立的指标体系通过25．68％的指标反映了97．09％的原始信息。通过与同类的指标体系进行对比,对大连市绿色产业评价指标体系的合理性做了进一步的分析。     

基于三角模糊熵的经济评价模型及副省级城市的实证研究

根据坚持以人为本,全面、协调、可持续的发展观的科学发展观的内涵,根据经济发展速度、经济发展质量及可持续发展等六个评价准则构建了经济评价指标体系。用客观赋权的熵值法和主观赋权的三角模糊法组合确定指标最优权重,建立了基于组合赋权的经济综合评价模型,并采用中国10个副省级城市的截面数据进行实证分析。本文的创新与特色一是通过对可持续发展和经济结构等准则层的指标评价,反映科学发展观全面协调可持续的基本要求。二是通过利用一个指标与另一个指标分值离差平方和最大原则,使各评价对象的多属性综合评价值尽可能分散、以便更清晰地体现指标对比的差异。改变了现有组合赋权研究中的由于平均赋权而导致指标差异不明显的状况。研究结果表明,根据我国10个典型副省级城市的截面数据、在指标分值离差平方和最大原则下建立优化模型,得到的主、客观权重分别为0.471和0.529。三是通过组合赋权,既保留了熵值法等客观赋权对实际情况的真实反映和对未来因素的推测、又反映了三角模糊等主观赋权所体现的指标属性对评价的重要程度和专家的知识与经验。四是对GDP增长率等适中型指标用偏离最优值的程度评分,解决了现有评价片面认为这些指标越大越好或越小越好的不合理问题。五是根...     

论正交法与三次设计中的综合评分

本文对正交法和三次设计中的多指标的综合评分问题进行了系统的讨论。对任何一个多指标评分问题，应首先用标准化方法解决指标间量纲不一致和指标间的矛盾性问题。然后针对指标间的重要性差异给出重要性权重，并通过给定信息量权重的方法来区别某指标指标值在各方案间的差异。最后是综合评分。对上面的每一步骤都给出了几种可选择的处理方法。     

单指标模型的统计推断

本文对单指标模型的统计推断方法进行了系统阐述,其中包括联系函数和指标系数的估计,经验似然,模型检验和变量选择等。本文的取材来自近二十年来的最新研究成果。     

单指标模型的统计推断

本文对单指标模型的统计推断方法进行了系统阐述,其中包括联系函数和指标系数的估计,经验似然,模型检验和变量选择等。本文的取材来自近二十年来的最新研究成果。     

陕西省县域经济差异及其极化效应分析

县域经济是省域国民经济的重要组成部分.选取2007-2016年陕西省103个县域单位的相关面板数据,运用多种指标方法对县域单位的发展差异及其极化效应进行分析,得出如下结论:①生产性指标的空间差异大于生活性指标,经济密度的空间差异大于人均GDP,农村居民收入的空间差异大于城镇居民收入.②三大区域的地区间差异明显小于地区内差异,生活性指标的极化效应呈下降趋势,人均GDP的极化效应明显减弱,而经济密度的极化效应稍有增强.③第二产业的县域间差异最大,它是导致县域经济分异的主要原因.影响县域经济差异的主要因素是产业集中效应与结构效应,产业集中效应抑制了县域经济的进一步分异,而结构效应对县域经济差异的影响是先促进后抑制.     

价格波动幅度变动率——一个新的市场风险度量指标

在度量风险时,一个常用的指标就是收益率的方差.然而,用该指标来度量风险会丢失掉一些非常有用的信息,如一日之内的开盘价,最高价,最低价等.鉴于传统指标的不足,提出用另一种指标---价格波动幅度变动率来度量市场风险.为了说明该指标的合理性及优良性,用标普500指数考察了该指标的主要经验统计特性和动态结构.结果表明新的指标具有近似正态性,建模的简易性及可预测性,因此可以作为一个新的风险度量指标.     

分数填补下两总体分位数差异的半经验似然推断

在完全随机缺失机制情形,利用分数填补法填补缺失值,然后用经验似然方法构造两总体分位数差异的半经验似然比统计量,证明其渐近服从加权X~2分布并构造了相应的半经验似然置信区间.     

EMPIRICAL LIKELIHOOD RATIO CONFIDENCE INTERVALS FOR VARIOUS DIFFERENCES OF TWO POPULATIONS

Recently the empirical likelihood has been shown to be very useful in nonparametric models. Qin combined the empirical likelihood thought and the parametric likelihood method to construct confidence intervals for the difference of two population means in a semiparametric model. In this paper, we use the empirical likelihood thought to construct confidence intervals for some differences of two populations in a nonparametric model. A version of Wilks' theorem is developed.     

Empirical likelihood for heteroscedastic partially linear models

We make empirical-likelihood-based inference for the parameters in heteroscedastic partially linear models. Unlike the existing empirical likelihood procedures for heteroscedastic partially linear models, the proposed empirical likelihood is constructed using components of a semiparametric efficient score. We show that it retains the double robustness feature of the semiparametric efficient estimator for the parameters and shares the desirable properties of the empirical likelihood for linear models. Compared with the normal approximation method and the existing empirical likelihood methods, the empirical likelihood method based on the semiparametric efficient score is more attractive not only theoretically but empirically. Simulation studies demonstrate that the proposed empirical likelihood provides smaller confidence regions than that based on semiparametric inefficient estimating equations subject to the same coverage probabilities. Hence, the proposed empirical likelihood is preferred to the normal approximation method as well as the empirical likelihood method based on semiparametric inefficient estimating equations, and it should be useful in practice.     

Jackknife-blockwise empirical likelihood methods under dependence

Empirical likelihood for general estimating equations is a method for testing hypothesis or constructing confidence regions on parameters of interest. If the number of parameters of interest is smaller than that of estimating equations, a profile empirical likelihood has to be employed. In case of dependent data, a profile blockwise empirical likelihood method can be used. However, if too many nuisance parameters are involved, a computational difficulty in optimizing the profile empirical likelihood arises. Recently, Li et al. (2011) [9] proposed a jackknife empirical likelihood method to reduce the computation in the profile empirical likelihood methods for independent data. In this paper, we propose a jackknife-blockwise empirical likelihood method to overcome the computational burden in the profile blockwise empirical likelihood method for weakly dependent data.     

二阶扩散模型的经验似然推断

本文利用经验似然方法得到了二阶扩散模型的漂移系数和扩散系数的经验似然估计量, 并研究这些估计量的相合性和渐近正态性. 进一步在经验似然方法的基础上给出了漂移系数和扩散系数的非对称的置信区间, 并且在一定的条件下证明了调整的对数似然比是渐近卡方分布的.     

Empirical likelihood based inference for semiparametric varying coefficient partially linear models with error-prone linear covariates

This paper considers statistical inference for semiparametric varying coefficient partially linear models with error-prone linear covariates. An empirical likelihood based statistic for parametric component is developed to construct confidence regions. The resulting statistic is shown to be asymptotically chi-square distributed. By the empirical likelihood ratio function, the maximum empirical likelihood estimator of the parameter is defined and the asymptotic normality is shown. A simulation experiment is conducted to compare the empirical likelihood, normal based and the naive empirical likelihood methods in terms of coverage accuracies of confidence regions.     

Empirical likelihood method for multivariate Cox regression

A unified empirical likelihood approach for three Cox-type marginal models dealing with multiple event times, recurrent event times and clustered event times is proposed. The resulting log-empirical likelihood ratio test statistics are shown to possess chi-squared limiting distributions. When making inferences, there is no need to solve estimating equations nor to estimate limiting covariance matrices. The optimal linear combination property for over-identified empirical likelihood is preserved by the proposed method and the property can be used to improve estimation efficiency. In addition, an adjusted empirical likelihood approach is applied to reduce the error rates of the proposed empirical likelihood ratio tests. The adjusted empirical likelihood tests could outperform the existing Wald tests for small to moderate sample sizes. The proposed approach is illustrated by extensive simulation studies and two real examples.     

基于经验似然的部分线性模型的统计诊断

经验似然方法己经被广泛应用于许多模型的统计推断.本文基于经验似然对部分线性模型进行统计诊断.首先给出模型的估计方程,进而得到模型参数的极大经验似然估计;其次,基于经验似然研究了三种不同的影响曲率;最后通过随机模拟和实例分析,说明了统计诊断方法的有效性.     

Smoothed jackknife empirical likelihood method for ROC curve

In this paper we propose a smoothed jackknife empirical likelihood method to construct confidence intervals for the receiver operating characteristic (ROC) curve. By applying the standard empirical likelihood method for a mean to the jackknife sample, the empirical likelihood ratio statistic can be calculated by simply solving a single equation. Therefore, this procedure is easy to implement. Wilks’ theorem for the empirical likelihood ratio statistic is proved and a simulation study is conducted to compare the performance of the proposed method with other methods.     

Empirical likelihood for median regression model with designed censoring variables

We propose a new and simple estimating equation for the parameters in median regression models with designed censoring variables, and then apply the empirical log likelihood ratio statistic to construct confidence region for the parameters. The empirical log likelihood ratio statistic is shown to have a standard chi-square distribution, which makes this method easy to implement. At the same time, another empirical log likelihood ratio statistic is proposed based on an existing estimating equation and the limiting distribution of the empirical likelihood ratio statistic is shown to be a sum of weighted chi-square distributions. We compare the performance of the empirical likelihood confidence region based on the new estimating equation, with that based on the existing estimating equation and a normal approximation method by simulation studies.     

Empirical likelihood-based inference in a partially linear model for longitudinal data

A partially linear model with longitudinal data is considered, empirical likelihood to infer- ence for the regression coefficients and the baseline function is investigated, the empirical log-likelihood ratios is proven to be asymptotically chi-squared, and the corresponding confidence regions for the pa- rameters of interest are then constructed. Also by the empirical likelihood ratio functions, we can obtain the maximum empirical likelihood estimates of the regression coefficients and the baseline function, and prove the asymptotic normality. The numerical results are conducted to compare the performance of the empirical likelihood and the normal approximation-based method, and a real example is analysed.