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1.
非线性回归模型的经验似然诊断   总被引:1,自引:0,他引:1  
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.  相似文献   

2.
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.  相似文献   

3.
部分线性变量含误差模型的经验似然估计   总被引:2,自引:0,他引:2  
马俊玲 《应用数学》2005,18(1):136-143
本文把经验似然方法推广到部分线性变量含误差模型 ,得到了Wilks定理的非参数形式 ,定理用来构造参数向量的渐近置信区间 .结果与WangandJing (1 999)对一般部分线性模型的经验似然结果加以比较 ,并且与正态逼近法得到的结果也作了比较 .  相似文献   

4.
黄玉  秦永松 《应用数学》2018,31(4):873-883
本文研究强混合样本下部分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,证明部分线性模型的参数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.  相似文献   

5.
部分线性度量误差模型(Partial linear measurement error model)是经典的部分线性模型的推广.在此模型中,我们假定解释变量含有度量误差.本文,我们把经验似然推广到部分线性度量误差模型,得到了非参数的Wilk's定理.我们的方法可以用来构建置信区间(域),也可以用来检验.数值模拟表明,我们的方法在构建的置信区间长度以及覆盖率方面有很好的结果.  相似文献   

6.
变系数部分线性模型的拟合优度检验   总被引:1,自引:0,他引:1  
本文考虑变系数部分线性模型的拟合优度检验问题.基于Profile经验似然方法,构造了参数部分和非参数部分的经验似然比检验统计量.并证明了其满足Wilks'现象,进而得到了一定置信水平的拒绝域.最后通过数据模拟,讨论了其检验功效.  相似文献   

7.
非线性回归模型中的约束拟似然   总被引:1,自引:0,他引:1  
韩郁葱 《大学数学》2005,21(3):45-51
在非线性回归模型中,拟得分函数是一类线性无偏估计函数中的最优者(GodambeandHeyde(1987),朱仲义(1996)),而由拟得分函数得到的拟似然估计在由线性无偏估计函数得到的估计类中具有渐近最优性(林路(1999)).本文则研究非线性回归模型中的有偏估计函数理论,构造了参数的约束拟似然估计,得到了约束拟似然的局部最优性,局部改进了拟似然估计,从而扩充了线性模型中的有偏估计理论.  相似文献   

8.
研究了在线性模型下,通过经验似然、欧氏似然及V_(T,P)方法分别检验了序列的相关性,并对三种序列相关检验方法做出了对比.同时进一步对三种序列相关性检验方法进行了数值模拟,模拟结果对未来做类似序列相关性检验的研究有着一定的参考意义.  相似文献   

9.
在缺失样本下,构造了线性模型中参数的调整的经验似然置信域,数值模拟表明调整的经验似然置信域有较好的覆盖率和精度.  相似文献   

10.
考虑响应变量带有缺失的部分线性模型,采用借补的思想,研究了参数部分和非参数部分的经验似然推断,证明了所提出的经验对数似然比统计量依分布收敛到χ2分布,由此构造参数部分和函数部分的置信域和逐点置信区间.对参数部分,模拟比较了经验似然与正态逼近方法;对函数部分,模拟了函数的逐点置信区间.  相似文献   

11.
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.  相似文献   

12.
We consider the standard linear multiple regression model in which the parameter of interest is the ratio of two regression coefficients. Our setup includes a broad range of applications. We show that the 1− α confidence interval for the interest parameter based on the profile, conditional profile, modified profile or adjusted profile likelihood can potentially become the entire real line, while appropriately chosen integrated likelihoods do not suffer from this drawback. We further explore the asymptotic length of confidence intervals in order to compare integrated likelihood-based proposals. The analysis is facilitated by an orthogonal parameterization.  相似文献   

13.
Approximation of parametric statistical models by exponential models is discussed, from the viewpoints of observed as well as of expected likelihood geometry. This extends a construction, in expected geometry, due to Amari. The approximations considered are parametrization invariant and local. Some of them relate to conditional models given exact or approximate ancillary statistics. Various examples are considered and the relation between the maximum likelihood estimators of the original model and the approximating models is studied.Research partly supported by the Danish Science Research Council.  相似文献   

14.
This paper is intended as an investigation of parametric estimation for the randomly right censored data. In parametric estimation, the Kullback-Leibler information is used as a measure of the divergence of a true distribution generating a data relative to a distribution in an assumed parametric model M. When the data is uncensored, maximum likelihood estimator (MLE) is a consistent estimator of minimizing the Kullback-Leibler information, even if the assumed model M does not contain the true distribution. We call this property minimum Kullback-Leibler information consistency (MKLI-consistency). However, the MLE obtained by maximizing the likelihood function based on the censored data is not MKLI-consistent. As an alternative to the MLE, Oakes (1986, Biometrics, 42, 177–182) proposed an estimator termed approximate maximum likelihood estimator (AMLE) due to its computational advantage and potential for robustness. We show MKLI-consistency and asymptotic normality of the AMLE under the misspecification of the parametric model. In a simulation study, we investigate mean square errors of these two estimators and an estimator which is obtained by treating a jackknife corrected Kaplan-Meier integral as the log-likelihood. On the basis of the simulation results and the asymptotic results, we discuss comparison among these estimators. We also derive information criteria for the MLE and the AMLE under censorship, and which can be used not only for selecting models but also for selecting estimation procedures.  相似文献   

15.
Given a model in algebraic statistics and data, the likelihood function is a rational function on a projective variety. Algebraic algorithms are presented for computing all critical points of this function, with the aim of identifying the local maxima in the probability simplex. Applications include models specified by rank conditions on matrices and the Jukes–Cantor models of phylogenetics. The maximum likelihood degree of a generic complete intersection is also determined.  相似文献   

16.
刁云霞  晏舒  丁洁丽 《数学学报》2018,61(6):1003-1020
在许多大型队列研究中,采用节约成本并能提高效率的抽样机制至关重要,基于因变量的抽样设计正是这样一种有偏抽样机制.这种方法最大的优点在于:能够将资源集中在那些包含有更多的协变量与因变量关系信息的研究群体上.本文研究基于因变量抽样设计下的线性模型中回归方程显著性检验以及回归系数显著性检验问题.基于一种半参数经验轮廓似然的方法,我们分别为回归方程检验与回归系数检验提出了相应的检验统计量,获得了所提出检验统计量的渐近性质.通过模拟研究评估了所提出的检验方法在有限样本下的表现,并应用提出的方法分析了一个孕妇分娩的实际数据.  相似文献   

17.
We consider the standard one-way ANOVA model; it is well-known that classical statistical procedures are based on a scalar non-centrality parameter. In this paper we explore both marginal likelihood and integrated likelihood functions for this parameter and we show that they exactly lead to the same answer. On the other hand, we prove that a fully Bayesian testing procedure may provide different conclusions, depending on what is considered to be the real quantity of interest in the model or, said differently, which are the competing hypotheses. We illustrate these issues via a real data example.  相似文献   

18.
We all know that we can use the likelihood ratio statistic to test hypotheses and construct confidence intervals in full parametric models. Recently, Owen (1988,Biometrika,75, 237–249; 1990,Ann. Statist.,18, 90–120) has introduced the empirical likelihood method in nonparametric models. In this paper, we combine these two likelihoods together and use the likelihood ratio to construct confidence intervals in a semiparametric problem, in which one model is parametric, and the other is nonparametric. A version of Wilks's theorem is developed.  相似文献   

19.
This paper is devoted to showing that likelihood inference on a statistical model which consists of mutually singular probability measures may be ineffective when the supports of those measures intersect. The problem is examined in an abstract metric setting appropriate for statistical inference about stochastic procesess. An important example is discussed: the estimation of the (constant) diffusion term of a stochastic differential equation.  相似文献   

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