Minimax Adaptive Generalized Ridge Regression Estimators
Minimax Adaptive Generalized Ridge Regression Estimators
复制标题
极小极大自适应广义岭回归估计器
DOI：
10.1080/01621459.1978.10480066
复制
发表时间：
1978
影响因子：
3.7
通讯作者：
W. Strawderman
中科院分区：
文献类型：
--
作者：
W. Strawderman
本文考虑了线性模型回归系数向量的广义岭估计问题，其中岭常数是根据数据选取的。对于一般的二次损失，我们产生这样的估计，其风险函数占主导地位的最小二乘程序提供的回归变量的数量至少是三个。本文将问题归结为多元正态分布均值向量的估计。在这种情况下，我们的极大极小估计的结果是独立的利益。
Abstract We consider the problem of estimating the vector of regression coefficients of a linear model using generalized ridge regression estimators where the ridge constant is chosen on the basis of the data. For general quadratic loss we produce such estimators whose risk function dominates that of the least squares procedure provided the number of regressors is at least three. We study the problem by the usual reduction to estimating the mean vector of a multivariate normal distribution. Our results on minimax estimation in this context are of independent interest.