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1.
在生长曲线模型中将设计阵的奇异值分解与普通的岭估计相结合,针对设计阵A与C至少有一个病态时的情况提出生长曲线模型中基于奇异值分解的岭估计.比较其在均方误差,均方误差矩阵,及PC准则下相对于最小二乘估计的优良性.证明其容许性并利用Hemmerle和Brantle用于确定广义岭估计参数的方法给出极小化均方误差的无偏估计法选取岭参数.  相似文献   

2.
对于聚集数据的多元线性模型,提出了参数的多元聚集广义岭估计的概念,给出了多元聚集广义岭估计相对于最小二乘估计及最佳线性无偏估计的两种相对效率,并得到了这两种相对效率的上界.  相似文献   

3.
对于聚集数据的多元线性模型,提出了参数的多元聚集综合岭估计的概念,给出了多元聚集综合岭估计相对于最小二乘估计及最佳线性无偏估计的两种相对效率,并得到了这两种相对效率的上界.应用Monte Carlo模拟,验证了有关结论是合理的.  相似文献   

4.
基于奇异值分解的岭型回归(英文)   总被引:3,自引:0,他引:3  
本文基于设计阵的奇异值分解(SVD),从LS估计出发,应用岭回归估计方法,构造了回归系数的一个新的有偏估计,称为基于SVD的岭型回归估计,简称RRSVD估计,讨论了其性质和偏参数的选取问题,得到了许多重要结论.计算结果表明,在设计阵呈病态时,RRS善岭回归估计.  相似文献   

5.
黄介武 《经济数学》2011,28(1):21-23
在一般多元线性模型中就基于岭估计的预测量与最优线性无偏预测量的最优性判别问题进行了讨论,得到了基于岭估计的预测量在矩阵迹意义下优于最优线性无偏预测量的充要条件.  相似文献   

6.
本文研究岭型主成分估计的回归最优性,证明了岭型降维估计类中。岭型主成分估计具有Φ-最小、E-最小和 D-最小性,且协方差阵的正交不变范数最小。推广了[2]中某些结果.  相似文献   

7.
对于随机效应部分为一般平衡多项分类的线性模型, 将王松桂等\ucite{1}提出的一种称之为谱分解估计(SDE)的参数估计新方法推广到具有病态设计阵的线性模型, 提出了部分岭型谱分解估计, 通过类似于主成分估计的降维模型变换, 可以很方便的研究它的抗干扰性和其它重要性质.本文的结果可以很方便的应用于Panel模型.  相似文献   

8.
本文对多元秩 序模型极大似然估计的存在性进行了研究,在对模型协方差阵Ω的一些约束下,文中给出了其参数极大似然估计存在的一些充分必要条件.  相似文献   

9.
本文研究了连续测量数据情况下的混合系数线性模型的参数估计问题.利用岭估计方法得到了该模型的几乎无偏岭估计,并证明了在均方误差意义下,几乎无偏岭估计优于岭估计.最后讨论了有偏参数的选取问题.  相似文献   

10.
大维数据给传统的协方差阵估计方法带来了巨大的挑战,数据维度和噪声的影响使得协方差阵的估计较为困难.在文章的研究中,将高频数据和低频数据相结合,提出了基于混合频率数据的协方差阵的估计和预测模型——MFD模型,MFD模型在解决了维数诅咒的同时还考虑了过去市场信息对协方差阵的影响,动态地估计和预测了未来的协方差阵.通过实证研究发现:较基于低频数据和高频数据的协方差阵估计和预测模型而言,MFD模型明显提高了高维协方差阵的估计和预测效率;并且将其应用在投资组合时,投资者获得了更高的投资收益和经济福利.  相似文献   

11.
In this paper we introduce the least-trimmed squares estimator for multivariate regression. We give three equivalent formulations of the estimator and obtain its breakdown point. A fast algorithm for its computation is proposed. We prove Fisher-consistency at the multivariate regression model with elliptically symmetric error distribution and derive the influence function. Simulations investigate the finite-sample efficiency and robustness of the estimator. To increase the efficiency of the estimator, we also consider a one-step reweighted estimator.  相似文献   

12.
A stochastic restricted ridge regression estimator   总被引:1,自引:0,他引:1  
Groß [J. Groß, Restricted ridge estimation, Statistics & Probability Letters 65 (2003) 57–64] proposed a restricted ridge regression estimator when exact restrictions are assumed to hold. When there are stochastic linear restrictions on the parameter vector, we introduce a new estimator by combining ideas underlying the mixed and the ridge regression estimators under the assumption that the errors are not independent and identically distributed. Apart from [J. Groß, Restricted ridge estimation, Statistics & Probability Letters 65 (2003) 57–64], we call this new estimator as the stochastic restricted ridge regression (SRRR) estimator. The performance of the SRRR estimator over the mixed estimator in respect of the variance and the mean square error matrices is examined. We also illustrate our findings with a numerical example. The shrinkage generalized least squares (GLS) and the stochastic restricted shrinkage GLS estimators are proposed.  相似文献   

13.
In this paper we consider the problem of estimating the matrix of regression coefficients in a multivariate linear regression model in which the design matrix is near singular. Under the assumption of normality, we propose empirical Bayes ridge regression estimators with three types of shrinkage functions, that is, scalar, componentwise and matricial shrinkage. These proposed estimators are proved to be uniformly better than the least squares estimator, that is, minimax in terms of risk under the Strawderman's loss function. Through simulation and empirical studies, they are also shown to be useful in the multicollinearity cases.  相似文献   

14.
In this paper, we propose a new biased estimator of the regression parameters, the generalized ridge and principal correlation estimator. We present its some properties and prove that it is superior to LSE (least squares estimator), principal correlation estimator, ridge and principal correlation estimator under MSE (mean squares error) and PMC (Pitman closeness) criterion, respectively.  相似文献   

15.
归庆明 《数学研究》1994,27(2):76-81
对于一类相依线性回归系统,本文提出了一种泛岭改进估计,并讨论了这种估计及相应的两步估计的优良性质,获得了若干深入的结果.  相似文献   

16.
In this short paper, we mainly aim to study the generalized ridge estimator in a linear regression model. Through matrix techniques including Hadamard product and derivative of a vector, the globally optimal generalized ridge estimator is derived under the generalized cross-validation criterion from the theoretical point of view. It will be seen that the notion of linearized ridge estimator plays an important role in the process. A numerical example is applied to illustrate the main results of the paper.  相似文献   

17.
SAS6.11版岭回归分析程序设计及其实例分析   总被引:9,自引:0,他引:9  
应用岭回归分析可以解决自变量之间存在复共线性时的回归问题。本文给出了在SAS6.1 1及以上版本中实现岭回归分析的程序 ,用具体实例说明进行岭回归的方法  相似文献   

18.
岭回归中确定K值的一种方法   总被引:7,自引:0,他引:7  
本文给出了岭估计中确定K值的一种新方法,这种方法改进了Hoerl和Kennard的相应方法。  相似文献   

19.
Efficiency of a Liu-type estimator in semiparametric regression models   总被引:1,自引:0,他引:1  
In this paper we consider the semiparametric regression model, y=Xβ+f+ε. Recently, Hu [11] proposed ridge regression estimator in a semiparametric regression model. We introduce a Liu-type (combined ridge-Stein) estimator (LTE) in a semiparametric regression model. Firstly, Liu-type estimators of both β and f are attained without a restrained design matrix. Secondly, the LTE estimator of β is compared with the two-step estimator in terms of the mean square error. We describe the almost unbiased Liu-type estimator in semiparametric regression models. The almost unbiased Liu-type estimator is compared with the Liu-type estimator in terms of the mean squared error matrix. A numerical example is provided to show the performance of the estimators.  相似文献   

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