The Linear Bayes Regression Estimator Under Weak Prior Assumptions
本文针对一元线性回归，在简单先验假设下显式推导出线性贝叶斯回归估计量及其风险，并证明其风险以n⁻¹速率降至所有线性拟合方法的最小风险。
A simple set of prior assumptions is given with respect to which the linear Bayes regression estimator, in the case of univariate regression, is determined explicitly. The risk, also determined explicitly, is shown to decrease,at rate 0 ( n-1 ), to the minimum risk attainable by any linear fitting procedure.