Fuzzy prior information and minimax estimation in the linear regression model

被引：0

作者：

Bernhard F. Arnold

Peter Stahlecker

机构：

[1] Universität Hamburg,Institut für Statistik und Ökonometrie

来源：

Statistical Papers | 1997年 / 38卷

关键词：

Membership Function; Linear Regression Model; Linear Estimator; Maximal Risk; Covariance Matrix Versus;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

We consider the linear regression modely=Xβ+u with prior information on the unknown parameter vector β. The additional information on β is given by a fuzzy set. Using the mean squared error criterion we derive linear estimators that optimally combine the data with the fuzzy prior information. Our approach generalizes the classical minimax procedure firstly proposed by Kuks and Olman.

引用

页码：377 / 391

页数：14

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