Some New Estimators for Small-Area Means With Application to the Assessment of Farmland Values
提出基于回归模型的小区域均值新估计量，用最大似然估计替代未知方差，通过SAS迭代计算，并改进稳健性。应用于玉米带非灌溉农田数据，预测均方误差低于传统调查估计量和其他回归型估计量。
Abstract Regression models that account for main state effects and nested county effects are considered for the assessment of farmland values. Empirical predictors obtained by replacing the unknown variances in the formulas of the optimal predictors by maximum likelihood estimates are presented. The computations are carried out by simple iterations between two SAS procedures. Estimators for the prediction variances are derived, and a modification to secure the robustness of the predictors is proposed. The procedure is applied to data on nonirrigated cropland in the Corn Belt states and is shown to yield predictors with considerably lower prediction mean squared errors than the survey estimators and other regression-type estimators. KEY WORDS: Components of varianceFitting constantsMixed modelsPrediction MSE