自变量优化的一元线性回归

Simple linear regression with independent variables optimized

一元线性回归是应用最为广泛的参数估计方法之一。文中提出一元线性回归的自变量在等差级数的基础上进行双向黄金分割,提高两端点观测值的多余观测分量,缩小观测值之间多余观测分量的差异,在不增加观测值数量和不改变观测值精度的前提下,提高稳健估计方法消除或减弱粗差的能力。

Simple linear regression is one of the most widely used methods of parameter estimation. The paper proposes a bidirectional golden section based on independent variables according to arithmetical progression, which increases the redundant observations of the observations at both endpoints and narrows the difference of redundant observations among the observations. Under the premise of not increasing the number of observations and changing observation accuracy, this method improves the capability of robust estimate method eliminating and weakening gross errors.