共查询到20条相似文献,搜索用时 15 毫秒
1.
考虑非参数协变量带有测量误差的非线性半参数模型,构造了模型中未知参数的经验对数似然比统计量,在测量误差分布为普通光滑分布时,证明了所提出的统计量具有渐近χ2分布,由此结果可以用来构造未知参数的置信域.另外也构造了未知参数的最小二乘估计量,并证明了它的渐近性质.就置信域及其覆盖概率大小方面,通过模拟研究比较了经验似然方法与最小二乘法的优劣. 相似文献
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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. 相似文献
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基于截面经验似然方法,将双重广义线性模型的拟似然估计方程作为截面经验似然比函数的约束条件,构造了均值模型和散度模型未知参数的置信区间.最后通过数据模拟,将该方法与正态逼近方法比较,说明了该方法是有效和可行的. 相似文献
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在一定的条件下证明了缺失数据情形基于分数填补方法得到的两非参数总体一般差异指标的经验似然比统计量的渐近分布为加权χ21,由此可构造差异指标的经验似然置信区间. 相似文献
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Empirical likelihood confidence regions of the parameters in a partially linear single-index model 总被引:5,自引:0,他引:5
XUE Liugen~ & ZHU Lixing~ . College of Applied Sciences Beijing University of Technology Beijing China . Department of Mathematics Hong Kong Baptist University Hong Kong China 《中国科学A辑(英文版)》2005,48(10):1333-1348
In this paper,a partially linear single-index model is investigated,and three empirical log-likelihood ratio statistics for the unknown parameters in the model are sug- gested.It is proved that the proposed statistics are asymptotically standard chi-square un- der some suitable conditions,and hence can be used to construct the confidence regions of the parameters.Our methods can also deal with the confidence region construction for the index in the pure single-index model.A simulation study indicates that,in terms of cov- erage probabilities and average areas of the confidence regions,the proposed methods perform better than the least-squares method. 相似文献
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In this paper, we consider the semiparametric regression model for longitudinal data. Due to the correlation within groups, a generalized empirical log-likelihood ratio statistic for the unknown parameters in the model is suggested by introducing the working covariance matrix. It is proved that the proposed statistic is asymptotically standard chi-squared under some suitable conditions, and hence it can be used to construct the confidence regions of the parameters. A simulation study is conducted to compare the proposed method with the generalized least squares method in terms of coverage accuracy and average lengths of the confidence intervals. 相似文献
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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
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Ranked-set sampling (RSS) often provides more efficient inference than simple random sampling (SRS). In this article, we propose
a systematic nonparametric technique, RSS-EL, for hypothesis testing and interval estimation with balanced RSS data using
empirical likelihood (EL). We detail the approach for interval estimation and hypothesis testing in one-sample and two-sample
problems and general estimating equations. In all three cases, RSS is shown to provide more efficient inference than SRS of
the same size. Moreover, the RSS-EL method does not require any easily violated assumptions needed by existing rank-based
nonparametric methods for RSS data, such as perfect ranking, identical ranking scheme in two groups, and location shift between
two population distributions. The merit of the RSS-EL method is also demonstrated through simulation studies.
This work was supported by National Natural Science Foundation of China (Grant No. 10871037) 相似文献
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ZHOU Xiuqing & WANG Jinde School of Mathematics Computer Science Nanjing Normal University Nanjing China. Department of Mathematics Nanjing University Nanjing China 《中国科学A辑(英文版)》2005,48(7):880-897
The least absolute deviations (LAD) estimation for nonlinear regression models with randomly censored data is studied and the asymptotic properties of LAD estimators such as consistency, boundedness in probability and asymptotic normality are established. Simulation results show that for the problems with censored data, LAD estimation performs much more robustly than the least squares estimation. 相似文献
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Empirical likelihood for partial linear models 总被引:2,自引:0,他引:2
In this paper the empirical likelihood method due to Owen (1988,Biometrika,75, 237–249) is applied to partial linear random models. A nonparametric version of Wilks' theorem is derived. The theorem is
then used to construct confidence regions of the parameter vector in the partial linear models, which has correct asymptotic
coverage. A simulation study is conducted to compare the empirical likelihood and normal approximation based method.
Research supported by NNSF of China and a grant to the first author for his excellent Ph.D. dissertation work in China.
Research supported by Hong Kong RGC CERG No. HKUST6162/97P. 相似文献
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Empirical likelihood-based inference in a partially linear model for longitudinal data 总被引:1,自引:0,他引:1
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. 相似文献
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This paper proposes some diagnostic tools for checking the adequacy of multivariate regression models including classical
regression and time series autoregression. In statistical inference, the empirical likelihood ratio method has been well known
to be a powerful tool for constructing test and confidence region. For model checking, however, the naive empirical likelihood
(EL) based tests are not of Wilks’ phenomenon. Hence, we make use of bias correction to construct the EL-based score tests
and derive a nonparametric version of Wilks’ theorem. Moreover, by the advantages of both the EL and score test method, the
EL-based score tests share many desirable features as follows: They are self-scale invariant and can detect the alternatives
that converge to the null at rate n
−1/2, the possibly fastest rate for lack-of-fit testing; they involve weight functions, which provides us with the flexibility
to choose scores for improving power performance, especially under directional alternatives. Furthermore, when the alternatives
are not directional, we construct asymptotically distribution-free maximin tests for a large class of possible alternatives.
A simulation study is carried out and an application for a real dataset is analyzed.
相似文献
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Empirical likelihood is a nonparametric method for constructing confidence intervals and tests,notably in enabling the shape of a confidence region determined by the sample data.This paper presents a new version of the empirical likelihood method for quantiles under kernel regression imputation to adapt missing response data.It eliminates the need to solve nonlinear equations,and it is easy to apply.We also consider exponential empirical likelihood as an alternative method.Numerical results are presented to compare our method with others. 相似文献
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In this paper, we propose a bias-corrected empirical likelihood (BCEL) ratio to construct a goodness- of-fit test for generalized linear mixed models. BCEL test maintains the advantage of empirical likelihood that is self scale invariant and then does not involve estimating limiting variance of the test statistic to avoid deteri- orating power of test. Furthermore, the bias correction makes the limit to be a process in which every variable is standard chi-squared. This simple structure of the process enables us to construct a Monte Carlo test proce- dure to approximate the null distribution. Thus, it overcomes a problem we encounter when classical empirical likelihood test is used, as it is asymptotically a functional of Gaussian process plus a normal shift function. The complicated covariance function makes it difficult to employ any approximation for the null distribution. The test is omnibus and power study shows that the test can detect local alternatives approaching the null at parametric rate. Simulations are carried out for illustration and for a comparison with existing method. 相似文献
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The aim of this paper is to show that existing estimators for the error distribution in non-parametric regression models can
be improved when additional information about the distribution is included by the empirical likelihood method. The weak convergence
of the resulting new estimator to a Gaussian process is shown and the performance is investigated by comparison of asymptotic
mean squared errors and by means of a simulation study.
相似文献