Logistic回归模型的经验似然诊断

经验似然方法已经被广泛用于许多模型的统计推断.基于经验似然对Logistic回归模型进行统计诊断.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;最后通过实例分析,说明了统计诊断方法的有效性.     

非线性回归模型的经验似然诊断

经验似然方法已经被广泛用于线性模型和广义线性模型.本文基于经验似然方法对非线性回归模型进行统计诊断.首先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度量;最后通过一个实际例子,说明了诊断方法的有效性.     

基于经验似然的AR(p)模型的统计诊断

本文基于经验似然方法对AR(p)模型进行统计诊断,文章首先给出p阶自回归模型的广义估计函数并对模型参数进行估计,然后运用数据删失、局部影响分析和伪残差方法对AR(p)模型进行统计诊断,最后通过实证来说明该诊断方法的有效性.     

部分线性模型的模态正交经验似然推断

本文考虑部分线性模型的有效经验似然统计推断问题.通过结合模态回归和正交投影技术,提出了一种模态经验似然统计推断过程.证明了提出的经验似然比函数渐近服从中心卡方分布,进而构造了模型参数的置信区间.所提出的估计方法可以对模型的参数分量和非参数分量分别估计,而互不影响,具有较好的稳健性和有效性.     

纵向数据均值和协方差矩阵的有效稳健估计（英文）

本文提出一种针对纵向数据回归模型下的均值和协方差矩阵同时进行的有效稳健估计.基于对协方差矩阵的Cholesky分解和对模型的改写,我们提出一个加权最小二乘估计,其中权重是通过广义经验似然方法估计出来的.所提估计的有效性得益于经验似然方法的优势,稳健性则是通过限制残差平方和的上界来达到.模拟研究表明,和已有的针对纵向数据的稳健估计相比,所提估计具有更高的效率和可比的稳健性.最后,我们把所提估计方法用来分析一组实际数据.     

缺失数据下估计方程的经验似然推断

将逆概率加权法和推广的逆概率加权法用于缺失数据下估计方程经验似然推断中,得到两种参数估计的渐近性质.同时可以得到两种方法所对应的估计方程是无偏的,相应的经验似然统计量都渐近卡方分布,从而避免的调整经验似然.数值模拟也进一步显示了两种方法的优势.     

基于ARFIMA-GARCH模型的混成检验

本文研究了ARFIMA-GARCH模型的混成检验问题.基于拟极大指数似然估计,给出了平方残差自相关函数的渐近性,进而建立了基于平方残差自相关函数的混成检验统计量.通过实例分析,表明可利用基于平方残差自相关函数的混成检验统计量来诊断检验由拟极大指数似然估计方法拟合的ARFIMA-GARCH模型.     

可加模型中参数的经验欧氏似然估计

可加模型是参数设计中一个非常重要,实用的模型。本文讨论了可加模型中参数的经验欧氏似然估计及其性质,并给出了一种与参数的经验欧氏似然估计渐近等效的加权LS估计,最后分析了一个数值例子。     

半参数模型的经验欧氏似然估计的大样本性质

本文证明了半参数模型的经验欧氏似然估计的强相合性和渐近正态性,还证明了经验欧氏似然比统计量的渐近x~2分布性,最后给出了几个例子。     

调整经验欧氏似然及其性质

经验(欧氏)似然是近年来非常流行的非参数统计方法之一,但其存在凸包限制和计算复杂等不足之处.针对此不足,CHEN等人(2008)给出调整经验似然.本文借助此想法,给出调整经验欧氏似然方法,进而讨论其相应的统计性质.理论结果显示,调整经验欧氏似然有与经验欧氏似然完全类似的性质;模拟结果显示,在某些情况下(如二维情况),调整经验(欧氏)似然所得的区间估计具有较好的覆盖率.此外,调整经验欧氏似然的思想和计算都比较简单.从实用角度看,具有较高的推广价值.     

Diagnostic Measures for Censored Linear Models based on Empirical Likelihood Method

In this paper, the diagnostic measures for censored linear models are studied based on the empirical likelihood method. First, the diagnostic measures for linear models are studied; Then, the censored linear models are converted to linear models, and the diagnostic measures for converted models are studied; Last, simulation studies and real data analysis are given to illustrate the validity of statistical diagnostic measures.     

Combined and Least Squares Empirical Likelihood

In conventional empirical likelihood, there is exactly one structural constraint for every parameter. In some circumstances, additional constraints are imposed to reflect additional and sought-after features of statistical analysis. Such an augmented scheme uses the implicit power of empirical likelihood to produce very natural adaptive statistical methods, free of arbitrary tuning parameter choices, and does have good asymptotic properties. The price to be paid for such good properties is in extra computational difficulty. To overcome the computational difficulty, we propose a least-squares version of the empirical likelihood. The method is illustrated by application to the case of combined empirical likelihood for the mean and the median in one sample location inference.     

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The additive model is a more flexible nonparametric statistical model which allows a data-analytic transform of the covariates.When the number of covariates is big and grows exponentially with the sample size the urgent issue is to reduce dimensionality from high to a moderate scale. In this paper, we propose and investigate marginal empirical likelihood screening methods in ultra-high dimensional additive models. The proposed nonparametric screening method selects variables by ranking a measure of the marginal empirical likelihood ratio evaluated at zero to differentiate contributions of each covariate given to a response variable. We show that, under some mild technical conditions, the proposed marginal empirical likelihood screening methods have a sure screening property and the extent to which the dimensionality can be reduced is also explicitly quantified. We also propose a data-driven thresholding and an iterative marginal empirical likelihood methods to enhance the finite sample performance for fitting sparse additive models. Simulation results and real data analysis demonstrate the proposed methods work competitively and performs better than competitive methods in error of a heteroscedastic case.     

Empirical Likelihood Inference for Functional Coefficient ARCH-M Model

Empirical likelihood inference for parametric and nonparametric parts in functional coefficient ARCH-M models is investigated in this paper. Firstly, the kernel smoothing technique is used to estimate coefficient function δ(x). In this way we obtain an estimated function with parameter β.Secondly, the empirical likelihood method is developed to estimate the parameter β. An estimated empirical log-likelohood ratio is proved to be asymptotically standard chi-squred, and the maximum empirical likelihood estimation(MELE) for β is shown to be asymptotically normal. Finally, based on the MELE of β, the empirical likelihood approach is again applied to reestimate the nonparametric part δ(x). The empirical log-likelohood ratio for δ(x) is proved to be also asymptotically standard chi-squred. Simulation study shows that the proposed method works better than the normal approximation method in terms of average areas of confidence regions for β, and the empirical likelihood confidence belt for δ(x) performs well.     

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 for mixed-effects error-in-variables model

This paper mainly introduces the method of empirical likelihood and its applications on two different models. We discuss the empirical likelihood inference on fixed-effect parameter in mixed-effects model with error-in-variables. We first consider a linear mixed-effects model with measurement errors in both fixed and random effects. We construct the empirical likelihood confidence regions for the fixed-effects parameters and the mean parameters of random-effects. The limiting distribution of the empirical log likelihood ratio at the true parameter is X2p＋q, where p, q are dimension of fixed and random effects respectively. Then we discuss empirical likelihood inference in a semi-linear error-in-variable mixed-effects model. Under certain conditions, it is shown that the empirical log likelihood ratio at the true parameter also converges to X2p＋q. Simulations illustrate that the proposed confidence region has a coverage probability more closer to the nominal level than normal approximation based confidence region.     

线性混合效应模型的正交经验似然推断

基于经验似然方法和QR分解技术, 对线性混合效应模型提出了一个基于正交经验似然的估计方法. 在一些正则条件下, 证明了所提出的经验对数似然比函数渐近服从卡方分布, 进而给出了模型固定效应的置信区间估计. 所提出估计过程不受模型随机效应的影响, 进而保证了所给出的估计是比较有效的. 一些数值模拟和实例分析进一步表明了所提出的估计方法是行之有效的.     

泊松回归模型中协变量含误差的经验似然推断

在模型的协变量含有测量误差的情况下,考虑一类泊松回归模型的统计推断问题.通过巧妙地构造辅助随机向量,提出一个工具变量类型的经验似然统计推断方法.证明构造的经验对数似然比函数渐近服从标准卡方分布,进而给出了回归系数的置信区间.所提出的估计方法可以有效地消除测量误差对估计精度的影响,并且具有较好的有限样本性质.     

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.