A Truncated Maximum Likelihood Estimator of a Constrained Bivariate Linear Regression Coefficient
研究了双变量线性回归中，当样本量和扰动项相关系数都较小时，约束最大似然估计的方差可能大于无约束估计，为此提出截断最大似然估计来选择合适估计量。
Abstract Abstract In a bivariate linear regression model, the constrained maximum likelihood estimator (MLE) of a regression coefficient usually has smaller variance than the unconstrained MLE. This situation can be reversed if both the sample size n and the correlation coefficient ρ of disturbances between two regression equations are small. That is, when both n and ρ are small, the variance of the unconstrained MLE is smaller than the constrained MLE. In this article, a truncated MLE is considered for the justification of using either the constrained or the unconstrained MLE as an appropriate estimator for the regression coefficient. Key Words: Bivariate regressionConstrained maximum likelihood estimationTruncated maximum likelihood estimationModal approximation