纵向数据下部分线性模型的经验似然推断

考虑纵向数据下部分线性模型,研究了回归系数和基准函数的经验似然推断,证明了所提出的经验对数似然比渐近于卡方分布,由此构造了相应兴趣参数的置信域和区间. 此外,利用经验似然比函数得到了回归系数和基准函数的最大经验似然估计,并且证明了所得估计量的渐近正态性.模拟研究比较了经验似然与正态逼近方法的有限样本性质,并进行了案例分析.     

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在φ混合的随机误差下,本文研究了固定设计及响应变量有缺失的非参数回归模型中回归函数的经验似然置信区间的构造.首先采用非参数回归填补法对缺失的数据进行填补,其次利用补足后得到的"完全样本"构造了非参数回归函数的经验似然比统计量,并证明了经验似然比统计量的极限分布为卡方分布,利用此结果可以构造非参数回归函数的经验似然置信区间.     

两样本密度比模型下的利用辅助信息的经验似然均值估计

在有限总体推断问题中,辅助总体信息是经常可获取的.经验似然方法己被证实是一种非常灵活和有用的工具来处理这类问题.在两样本密度比模型下,本文考虑了基准分布的总体均值的经验似然推断问题.对基于密度比模型的经验似然而言,对偶似然是一种便利的技术工具,尽管它与标准的经验似然具有相同的极值点和极值,但是它却不能方便地把此类辅助信息引入到似然函数里,因此会导致效率损失.相对而言,Qin和Lawless~([21])提出的标准的经验似然方法不会有此问题,且能方便地引入辅助信息.基于使用辅助信息的经验似然和对偶似然方法,我们构建了点估计和区间估计,并做了仔细的比较.模拟发现,尽管使用辅助信息的经验似然方法得到的点估计的效率提升很小,但是区间估计在一些情形下却有明显的差别.拿覆盖精度来说,在无偏或适当有偏的总体分布下,两种方法得到的区间估计是可比的,但当总体严重有偏时,前者的区间估计明显优于后者.     

缺失数据下φ混合样本情形非参数回归函数的经验似然推断

在φ混合的随机误差下,本文研究了固定设计及响应变量有缺失的非参数回归模型中回归函数的经验似然置信区间的构造.首先采用非参数回归填补法对缺失的数据进行填补,其次利用补足后得到的"完全样本"构造了非参数回归函数的经验似然比统计量,并证明了经验似然比统计量的极限分布为卡方分布,利用此结果可以构造非参数回归函数的经验似然置信区间.     

随机右删失数据下半参数线性变换模型的经验似然推断

本文研究了响应变量随机右删失情形下半参数线性变换模型的经验似然推断问题.构造了参数的经验似然比检验统计量,证明了经验似然比检验统计量的渐近分布为加权卡方分布.在此基础上,对经验似然比检验统计量进行了调整,证明了调整的经验似然比检验统计量的渐近分布为标准的卡方分布.基于经验似然和调整的经验似然方法,分别给出了回归参数的一定置信水平的置信域.最后对本文的方法和传统的正态逼近方法进行了模拟比较,模拟结果显示,从置信域的大小和经验覆盖概率两个角度看,本文的方法均比正态逼近方法优越.     

随机右删失数据下半参数线性变换模型的经验似然推断北大核心CSCD

本文研究了响应变量随机右删失情形下半参数线性变换模型的经验似然推断问题.构造了参数的经验似然比检验统计量,证明了经验似然比检验统计量的渐近分布为加权卡方分布.在此基础上,对经验似然比检验统计量进行了调整,证明了调整的经验似然比检验统计量的渐近分布为标准的卡方分布.基于经验似然和调整的经验似然方法,分别给出了回归参数的一定置信水平的置信域.最后对本文的方法和传统的正态逼近方法进行了模拟比较,模拟结果显示,从置信域的大小和经验覆盖概率两个角度看,本文的方法均比正态逼近方法优越.     

纵向数据下部分非线性模型的广义经验似然推断

本文研究了纵向数据下部分非线性模型中未知参数的置信域的构造.利用经验似然方法,构造了非线性函数中未知参数的广义对数经验似然比统计量,证明了其渐近于卡方分布.同时,得到了未知参数的最大经验似然估计,并证明了其渐近正态性.     

核实数据下非线性EV模型中经验似然降维推断

本文研究了响应变量有误差的非线性模型.应用半参数降维技术构造未知参数的被估计经验似然及调整的经验似然,证明了所提出的被估计的经验对数似然与其调整的经验对数似然分别渐近于独立卡方变量加权和的分布与标准卡方分布,所得结果可用来构造未知参数的置信域.     

强混合样本下随机设计情形线性模型的经验似然推断

本文研究强混合样本下随机设计情形线性模型的经验似然推断,将分块技术应用到经验似然方法中,证明了线性模型的参数β的对数经验似然比统计量的渐近分布为卡方分布,由此构造了强混合样本下β的经验似然置信区间.在有限样本情况下给出数值模拟结果.     

部分函数线性模型的经验似然推断

本文考虑部分函数线性回归模型,研究了回归系数的经验似然推断,证明了所提出的经验对数似然比渐近于χ~2分布,此结果可以用来构造了相应兴趣参数的置信域.另外,本文也给出了系数函数的极大经验似然估计,并在适当条件下给出了所提出估计量的收敛速度.仅就置信域精度及其覆盖概率大小方面,通过模拟研究和实例分析比较了经验似然方法与最小二乘方法的优劣.     

Comparison of two variances under inequality constraints by using empirical likelihood method

In this article, the empirical likelihood introduced by Owen Biometrika, 75, 237-249 （1988） is applied to test the variances of two populations under inequality constraints on the parameter space. One reason that we do the research is because many literatures in this area are limited to testing the mean of one population or means of more than one populations; the other but much more important reason is： even if two or more populations are considered, the parameter space is always without constraint. In reality, parameter space with some kind of constraints can be met everywhere. Nuisance parameter is unavoidable in this case and makes the estimators unstable. Therefore the analysis on it becomes rather complicated. We focus our work on the relatively complicated testing issue over two variances under inequality constraints, leaving the issue over two means to be its simple ratiocination. We prove that the limiting distribution of the empirical likelihood ratio test statistic is either a single chi-square distribution or the mixture of two equally weighted chi-square distributions.     

Penalized Empirical Likelihood Estimation for Partially Linear Single Index Panel Model with Fixed Effects

This paper constructs a penalized empirical likelihood estimation method via quadratic inference function method, filter method and empirical likelihood estimation method. Under some regular conditions, we derived the large sample properties of estimators and show that the proposed empirical likelihood ratio is asymptotically to chi-square distribution. Furthermore, the infinite sample performance of the proposed method is evaluated by Monte Carlo simulation and real data analysis.     

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??This paper constructs a penalized empirical likelihood estimation method via quadratic inference function method, filter method and empirical likelihood estimation method. Under some regular conditions, we derived the large sample properties of estimators and show that the proposed empirical likelihood ratio is asymptotically to chi-square distribution. Furthermore, the infinite sample performance of the proposed method is evaluated by Monte Carlo simulation and real data analysis.     

Empirical Likelihood for a Class of Functionals of Survival Distribution with Censored Data

The empirical likelihood was introduced by Owen, although its idea originated from survival analysis in the context of estimating the survival probabilities given by Thomas and Grunkemeier. In this paper, we investigate how to apply the empirical likelihood method to a class of functionals of survival function in the presence of censoring. We define an adjusted empirical likelihood and show that it follows a chi-square distribution. Some simulation studies are presented to compare the empirical likelihood method with the Studentized-t method. These results indicate that the empirical likelihood method works better than or equally to the Studentized-t method, depending on the situations.     

MA$(q)$模型的经验似然推断

In this article we study the empirical likelihood inference for MA（q） model. We propose the moment restrictions, by which we get the empirical likelihood estimator of the model parameter, and we also propose an empirical log-likelihood ratio based on this estimator. Our result shows that the EL estimator is asymptotically normal, and the empirical log-likelihood ratio is proved to be asymptotical standard chi-square distribution.     

弱相依数据中的分组经验Cressie-Read似然方法

本文考虑一般的弱相依数据, 提出了分组经验Cressie-Read似然方法. 得到了分组经验Cressie-Read似然参数估计的强收敛性、渐近正态性和其分组经验Cressie-Read统计量的渐近$\chi^{2}$性.     

Empirical likelihood for conditional quantile with left-truncated and dependent data

In this paper, we employ the method of empirical likelihood to construct confidence intervals for a conditional quantile in the presence and absence of auxiliary information, respectively, for the left-truncation model. It is proved that the empirical likelihood ratio admits a limiting chi-square distribution with one degree of freedom when the lifetime observations with multivariate covariates form a stationary α-mixing sequence. For the problem of testing a hypothesis on the conditional quantile, it is shown that the asymptotic power of the test statistic based on the empirical likelihood ratio with the auxiliary information is larger than that of the one based on the standard empirical likelihood ratio. The finite sample performance of the empirical likelihood confidence intervals in the presence and absence of auxiliary information is investigated through simulations.     

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.