A distribution-free least squares estimator for censored linear regression models
A distribution-free least squares estimator for censored linear regression models
复制标题
用于删失线性回归模型的无分布最小二乘估计器
DOI：
10.1016/0304-4076(86)90012-6
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发表时间：
1986
期刊：
影响因子：
--
通讯作者：
J. Horowitz
中科院分区：
文献类型：
--
作者：
J. Horowitz
本文描述了一种同时估计删失线性回归模型的系统分量的参数向量和随机分量的分布函数的方法。根据估计的参数向量和分布函数，通过最小化因变量的观测值与该变量相应的期望值之间的差值的平方和来获得估计量。所得的最小二乘参数估计器结合了可从估计样本获得的回归模型的随机分量的分布信息。因此，它通常可能比不使用此类信息的参数估计器更有效。使用最小二乘估计器的数值实验结果倾向于支持这一假设。
This paper describes a method for estimating simultaneously the parameter vector of the systematic component and the distribution function of the random component of a censored linear regression model. The estimator is obtained by minimizing the sum of the squares of the differences between the observed values of the dependent variable and the corresponding expected values of this variable according to the estimated parameter vector and distribution function. The resulting least squares parameter estimator incorporates information on the distribution of the random component of the regression model that is available from the estimation sample. Hence, it may often be more efficient than are parameter estimators that do not use such information. The results of numerical experiments with the least squares estimator tend to support this hypothesis.