Small-Sample Properties of Estimators of Regression Coefficients Given a Common Pattern of Missing Data
针对一种常见的缺失数据模式，用非似然方法推导回归系数估计量，在正态假设下研究其小样本性质，证明无偏性、给出精确方差公式，并与普通最小二乘和最大似然估计量比较效率。
For a commonly occurring pattern of missing data, estimators of regression coefficients are derived by a non-likelihood method. The small-sample properties are investigated for the case of normality assumptions. The estimators are shown to be unbiased, exact small-sample variance formulae are derived, comparisons are made with ordinary least-squares estimators and it is demonstrated that the estimators can be more efficient than maximum-likelihood estimators in small samples.