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1.
二次损失下随机回归系数和参数的线性Minimax估计 总被引:3,自引:0,他引:3
对带有随机效应的一般线性模型,本文提出了随机回归系数和参数线性组合的Minimax估计问题.在二次损失下,研究了线性估计的极小极大性.关于适当的假设,得到了可估函数的唯一线性Minimax估计. 相似文献
2.
二次损失下随机回归系数和参数的线性估计是可容许的充要条件 总被引:11,自引:0,他引:11
本文对于一般的随机效应线性模型(包括混合效应线性模型),在二次损失函数下给出了随机回归系数和参数的线性可估函数的齐次线性估计(线性估计)在齐次线性估计类(线性估计类)中可容许的充分必要条件. 相似文献
3.
该文在一般正态随机效应线性模型中研究了随机回归系数和参数的估计问题. 在二次损失下,得到了线性可估函数在一切估计类中的唯一Minimax估计. 相似文献
4.
该文在一般线性混合模型中, 研究了固定和随机效应线性组合的估计问题.对观测向量的协方差阵可以为奇异矩阵情形下,导出了该组合的最佳线性无偏估计,并证明了它的唯一性.在一般线性混合模型的特例, 三个小域模型下, 得到了小域均值ui 和方差分量的谱分解估计. 进而, 获得了基于谱分解估计的两步估计均方误差的二阶逼近. 相似文献
5.
多元线性模型中随机回归系数和参数的线性估计的泛容许性 总被引:7,自引:0,他引:7
本文对于一般的随机效应多元线性模型,给出了随机回归系数和参数的线性可估函数的泛容许性估计的定义,并得到了随机回归系数和参数的线性可估函数的齐线性估计在齐线性估计类中泛容许性的特征。 相似文献
6.
研究了部分线性回归模型附加有随机约束条件时的估计问题.基于Profile最小二乘方法和混合估计方法提出了参数分量随机约束下的Profile混合估计,并研究了其性质.为了克服共线性问题,构造了参数分量的Profile混合岭估计,并给出了估计量的偏和方差. 相似文献
7.
在一些较弱的充分条件下,本文研究了误差为随机适应序列下,线性模型回归参数M估计的强相合性.与文献中已有结果比较,扩大了应用范围,且对矩条件也有较大改进.同时我们给出了随机适应误差下线性模型参数M估计的渐近正态性. 相似文献
8.
《数学的实践与认识》2013,(21)
本文借用线性模型系数的Minimax估计方法,在二次损失函数下运用随机优化理论对Gua.ss-Markov非线性模型的系数进行了研究,建立了非线性模型系数Minimax估计的随机优化模型. 相似文献
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本文首先研究了含三个方差分量的线性混合随机效应模型改进的ANOVA估计, 此估计在均方损失下一致优于ANOVA估计. 由于这些方差估计取负值的概率大于零, 对得到的估计在某非负点采用截尾的方法得到非负估计是一种常用的方法. 对文章中提出的估计, 研究了此估计在某非负点截尾之后得到的估计在均方损失意义下优于截尾之前的估计的充分条件, 同时给出ANOVA估计在截尾之后优于它本身的充分条件, 而且将得到的结论推广到更一般的线性混合随机效应模型. 相似文献
11.
Li Wen XU Song Gui WANG 《数学学报(英文版)》2007,23(3):497-506
In this paper, the authors address the problem of the minimax estimator of linear combinations of stochastic regression coefficients and parameters in the general normal linear model with random effects. Under a quadratic loss function, the minimax property of linear estimators is investigated. In the class of all estimators, the minimax estimator of estimable functions, which is unique with probability 1, is obtained under a multivariate normal distribution. 相似文献
12.
We consider the simultaneous linear minimax estimation problem in linear models with ellipsoidal constraints imposed on an unknown parameter. Using convex analysis, we derive necessary and sufficient optimality conditions for a matrix to define the linear minimax estimator. For certain regions of the set of characteristics of linear models and constraints, we exploit these optimality conditions and get explicit formulae for linear minimax estimators. 相似文献
13.
矩阵损失下随机回归系数和参数的线性Minimax估计 总被引:2,自引:0,他引:2
对于一般的随机效应线性模型Y=Xβ+ε,这里β和ε分别是p维和n维的随机向量,且E(βε)=(Aa0),Cov(βε)=σ2(V10
0V2),(Vi≥0,i=1,2)我们定义了Sα+Qβ的线性Minimax估计,在一定条件下得到了Sα+Qβ在线性估计类中的Minimax估计,并在几乎处处意义下证明了它的唯一性. 相似文献
14.
Jianqing Fan Theo Gasser Irène Gijbels Michael Brockmann Joachim Engel 《Annals of the Institute of Statistical Mathematics》1997,49(1):79-99
We consider local polynomial fitting for estimating a regression function and its derivatives nonparametrically. This method possesses many nice features, among which automatic adaptation to the boundary and adaptation to various designs. A first contribution of this paper is the derivation of an optimal kernel for local polynomial regression, revealing that there is a universal optimal weighting scheme. Fan (1993, Ann. Statist., 21, 196-216) showed that the univariate local linear regression estimator is the best linear smoother, meaning that it attains the asymptotic linear minimax risk. Moreover, this smoother has high minimax risk. We show that this property also holds for the multivariate local linear regression estimator. In the univariate case we investigate minimax efficiency of local polynomial regression estimators, and find that the asymptotic minimax efficiency for commonly-used orders of fit is 100% among the class of all linear smoothers. Further, we quantify the loss in efficiency when going beyond this class. 相似文献
15.
Karsten Schmidt 《Acta Appl Math》1996,43(1):127-138
We consider the linear regression model where prior information in the form of linear inequalities restricts the parameter space to a polyhedron. Since the linear minimax estimator has, in general, to be determined numerically, it was proposed to minimize an upper bound of the maximum risk instead. The resulting so-called quasiminimax estimator can be easily calculated in closed form. Unfortunately, both minimax estimators may violate the prior information. Therefore, we consider projection estimators which are obtained by projecting the estimate in an optional second step. The performance of these estimators is investigated in a Monte Carlo study together with several least squares estimators, including the inequality restricted least squares estimator. It turns out that both the projected and the unprojected quasiminimax estimators have the best average performance. 相似文献
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17.
S. V. Tatarinov 《Journal of Mathematical Sciences》1992,60(3):1519-1525
We consider minimax estimation of a linear functional of a homogeneous random field. A linear optimal estimator of the functional is derived and the field achieving the minimax error of the functional estimate is determined.Translated from Vychislitel'naya i Prikladnaya Matematika, No. 59, pp. 105–113, 1986. 相似文献
18.
在二次损失下关于任意矩阵V对G-M模型讨论了齐次线性估计类中可估函数的条件Mimimax估计与性质。 相似文献
19.
Kurt Hoffmann 《Acta Appl Math》1996,43(1):87-95
In the linear regression model with ellipsoidal parameter constraints, the problem of estimating the unknown parameter vector is studied. A well-described subclass of Bayes linear estimators is proposed in the paper. It is shown that for each member of this subclass, a generalized quadratic risk function exists so that the estimator is minimax. Moreover, some of the proposed Bayes linear estimators are admissible with respect to all possible generalized quadratic risks. Also, a necessary and sufficient condition is given to ensure that the considered Bayes linear estimator improves the least squares estimator over the whole ellipsoid whatever generalized risk function is chosen. 相似文献
20.
We consider an approach yielding a minimax estimator in the linear regression model with a priori information on the parameter vector, e.g., ellipsoidal restrictions. This estimator is computed directly from the loss function and can be motivated by the general Pitman nearness criterion. It turns out that this approach coincides with the projection estimator which is obtained by projecting an initial arbitrary estimate on the subset defined by the restrictions. 相似文献