Minimum Distance Methods Based on Quadratic Distances for Transforms in Simple Linear Regression Models
本文在简单线性回归模型中引入了基于二次距离的变换最小距离估计量和检验统计量，推导了误差分布估计量和检验的渐近性质，并证明了修正的二次距离估计量在误差分布对称且对数密度导数为变换线性组合时是有效的。
SUMMARY Minimum distance estimators and test statistics based on quadratic distances for transforms are introduced in simple linear regression models. Asymptotic properties of quadratic distance estimators and tests for the error distributions are derived. The modified quadratic distance estimator is shown to be efficient provided that the error distribution is symmetric and the derivative of the logarithm of the error density is a linear combination of the transforms.