Best Linear Unbiased Estimators for Repeated Surveys
本文用最小二乘理论统一处理重复抽样调查中最佳线性无偏估计量的获取问题，澄清了不同方法间的关系，并探讨了时间序列估计量在实际应用中的问题，特别是平稳性的作用。
Summary Recent work on the problem of obtaining best linear unbiased estimators from a sample survey which is repeated on several occasions has centred on the effects of extending the model assumptions to allow for stochastic variation in the parameters being estimated. In this paper a unified approach to the problem is given using least squares theory. The results of Blight and Scott (1973) are extended, and the relationships between their results, those of Scott and Smith (1974) and the classical approach of Patterson (1950), or its generalization by Gurney and Daly (1965), are clarified. The problems involved in putting the time series estimators into practice are examined, with particular reference to the role of stationarity.