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1.
增长曲线模型中UMRE估计的存在性   总被引:2,自引:0,他引:2  
对于设计矩阵不满秩,协方差阵任意或具有均匀结构或序列结构的正态增长曲线模型,本文讨论参数矩阵的一致最小风险同变(UMng)估计的存在性.在仿射变换群GI和转移交换群、二次损失和矩阵损失下本文分别获得存在回归系数矩阵的线性可估函数矩阵的UMRE估计的充要条件,推广了由[21]给出的在设计矩阵满秩下估计回归系数矩阵的结果.本文还首次证明了在群G1和二次损失下不存在协方差阵V和trV的UMRE估计.  相似文献   

2.
具有特殊协方差结构的 SURE 模型中参数估计的若干结果   总被引:1,自引:0,他引:1  
本文讨论具有特殊协方差结构似乎不相关回归方程(SURE)模型中参数的估计问题.除非另有说明,损失函数将取为二次损失和矩阵损失.本文证明了回归系数的线性可估函数的最小二乘估计是极小极大的且在矩阵损失函数下是可容许的;还分别在仿射交换群和平移群下导出了存在回归系数的线性可估函数的一致最小风险同变(UMRE)估计的充要条件,并证明了在仿射交换和二次损失下不存在协方差阵和方差的UMRE估计.  相似文献   

3.
对于协方差阵任意或具有均匀结构或具有序列结构的正态增长曲线模型,在仿射变换群和转移交换群、二次损失和矩阵损失下,分别获得了存在回归系数矩阵的一致最小风险同变(UMRE)估计的充要条件.  相似文献   

4.
SURE模型中参数的UMRE估计的一个注记   总被引:2,自引:0,他引:2  
本文考虑似乎不相关回归方程组(SURE)模型在设计阵不满秩情形下回归系数的一致最小风险同变(UMRE)估计。给出仿射变换群,转移变换群各自在二次损失和矩阵损失下回归系数可估函数存在UMRE估计的充要条件。  相似文献   

5.
在二次矩阵损失函数下研究了协方差矩阵未知的多元线性模型中回归系数矩阵的可估线性函数的矩阵非齐次线性估计的可容许性,给出了矩阵非齐次线性估计在线性估计类中可容许的一个充要条件.  相似文献   

6.
研究了一类正态线性模型参数的一致最小风险同变(UMRE)估计的存在性. 这类模型包含了正态方差分量模型、增长曲线模型、 扩充的增长曲线模型以及似乎不相关回归方程组等. 在这类模型、仿射变换群、二次损失或矩阵损失下, 分别导出了回归系数的线性可估函数、协方差阵V和(trV)α(α>0已知)的UMRE估计存在的充分必要条件. 利用这些结果可导出文献中有关(扩充)增长曲线模型和似乎不相关回归方程组中估计回归系数的结果,并把协方差阵V和trV的UMRE估计不存在的充分条件发展成充分必要条件. 此外, 导出了方差分量模型中参数的UMRE估计存在的充分必要条件.  相似文献   

7.
一般Gauss-Markov模型中可估函数的线性Minimax估计   总被引:5,自引:0,他引:5  
设Y是具有均值Xβ和协方差阵σ2V的n维随机向量,Sβ是线性可估函数,这里X,S和V≥0是已知矩阵,β∈Rp和σ2>0是未知参数.本文分别在给定的矩阵损失和二次损失下研究了线性估计的Minimax性.在适当的假设下,得到了Sβ的唯一线性Minimax估计(有关唯一性在几乎处处意义下理解).  相似文献   

8.
矩阵损失下一般Gauss-Markov模型中回归系数的线性MINIMAX估计   总被引:10,自引:0,他引:10  
设Y是具有均值Xβ和协方差阵σ2V的n维随机向量,Sβ是线性可估函数,这里X,S和V0是已知矩阵,β∈Rp和σ2>0是未知参数.本文在矩阵损失下研究了线性估计的Minimax性.在适当的假设下,得到了Sβ的唯一线性Minimax估计(有关唯一性在几乎处处意义下理解).  相似文献   

9.
在二次损失函数下,本文给出了正态方差最优同变估计的一个新的改进估计,并证明了常用正态协方差和协方差阵的估计都是非容许估计。  相似文献   

10.
研究一类线性模型下参数估计的若干问题.这类模型包含了多个因变量线性模型、增长曲线模型、扩充的增长曲线模型、似乎不相关回归方程组、方差分量模型等常用模型.在这类线性模型下,证明了当误差服从多元t分布时与误差服从多元正态分布时,具有相同的完全统计量和无偏估计,且在后一种情况下的充分统计量必为前一种情况下的充分统计量.对于带有多种协方差结构的前述几种模型,把在误差服从多元正态分布下,相应的协方差阵及有关参数的一致最小风险无偏(UMRU)估计存在性的结论推广到了相应的误差服从多元t分布情形.此外,对于误差服从多元t分布的这类统一的线性模型,给出了回归系数的线性可估函数的无偏估计的协方差阵的C-R下界.  相似文献   

11.
The robustness of regression coefficient estimator is a hot topic in regression analysis all the while. Since the response observations are not independent, it is extraordinarily difficult to study this problem for random effects growth curve models, especially when the design matrix is non-full of rank. The paper not only gives the necessary and sufficient conditions under which the generalized least square estimate is identical to the the best linear unbiased estimate when error covariance matrix is an arbitrary positive definite matrix, but also obtains a concise condition under which the generalized least square estimate is identical to the maximum likelihood estimate when the design matrix is full or non-full of rank respectively. In addition, by using of the obtained results, we get some corollaries for the the generalized least square estimate be equal to the maximum likelihood estimate under several common error covariance matrix assumptions. Illustrative examples for the case that the design matrix is full or non-full of rank are also given.  相似文献   

12.
In this paper, we establish a group of closed-form formulas for the maximal and minimal ranks of a nonlinear matrix expression with respect to two variant matrices by using a linearization method and some known formulas for extremal ranks of linear matrix expressions. In addition, by using some pure algebraic operations of matrices and their generalized inverses, we derive the maximal and minimal ranks of the above nonlinear matrix expression, where the two variant matrices are any solutions of two consistent matrix equations. As an application, we derive some sufficient and necessary conditions for the existence of the solution of a nonlinear matrix function.  相似文献   

13.
This article investigates linear minimax estimators of regression coefficient in a linear model with an assumption that the underlying distribution is a normal one with a nonnegative definite covariance matrix under a balanced loss function. Some linear minimax estimators of regression coefficient in the class of all estimators are obtained. The result shows that the linear minimax estimators are unique under some conditions.  相似文献   

14.
在平衡损失函数下,主要研究回归系数的线性Minimax估计问题.通过分析平衡损失风险的极大极小性,得到了线性优化计类中回归函数的Minimax估计.在适当的假设下,证明了其唯一性.  相似文献   

15.
本文研究设计矩人有相同值域的相依回归模型,在矩阵损失下我们给出了回归系数的线性估计是线性容许的充要条件,它们推广了已有的结果,我们也在矩阵上给出了某个回归模型的回归系数的唯一的Minimax估计,它说明此时其它模型的信息不起作用  相似文献   

16.
多维金融高频协方差阵预测模型的比较分析   总被引:1,自引:0,他引:1  
现代投资组合理论大部分是从组合风险控制的角度展开,协方差矩阵扮演着非常重要的角色.将高频协方差阵应用在投资组合或风险管理时,就需要考虑采用何种预测模型来对高频协方差阵进行预测,较好的预测模型能够更加准确的对资产的波动性进行预测.高频协方差阵预测模型的建立较为复杂,目前还没有一种广泛被认可的模型.采用MCS检验法来选择最优的预测模型,研究发现高频协方差阵预测模型LOG-HAR模型在所有的损失函数下预测能力最好,并且高频协方差阵预测模型的预测能力要优于低频协方差阵预测模型.  相似文献   

17.
For statistical inferences that involve covariance matrices, it is desirable to obtain an accurate covariance matrix estimate with a well-structured eigen-system. We propose to estimate the covariance matrix through its matrix logarithm based on an approximate log-likelihood function. We develop a generalization of the Leonard and Hsu log-likelihood approximation that no longer requires a nonsingular sample covariance matrix. The matrix log-transformation provides the ability to impose a convex penalty on the transformed likelihood such that the largest and smallest eigenvalues of the covariance matrix estimate can be regularized simultaneously. The proposed method transforms the problem of estimating the covariance matrix into the problem of estimating a symmetric matrix, which can be solved efficiently by an iterative quadratic programming algorithm. The merits of the proposed method are illustrated by a simulation study and two real applications in classification and portfolio optimization. Supplementary materials for this article are available online.  相似文献   

18.
The problem of estimating large covariance matrices of multivariate real normal and complex normal distributions is considered when the dimension of the variables is larger than the number of samples. The Stein–Haff identities and calculus on eigenstructure for singular Wishart matrices are developed for real and complex cases, respectively. By using these techniques, the unbiased risk estimates for certain classes of estimators for the population covariance matrices under invariant quadratic loss functions are obtained for real and complex cases, respectively. Based on the unbiased risk estimates, shrinkage estimators which are counterparts of the estimators due to Haff [L.R. Haff, Empirical Bayes estimation of the multivariate normal covariance matrix, Ann. Statist. 8 (1980) 586–697] are shown to improve upon the best scalar multiple of the empirical covariance matrix under the invariant quadratic loss functions for both real and complex multivariate normal distributions in the situation where the dimension of the variables is larger than the number of samples.  相似文献   

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