Fast Computation of Auxiliary Quantities in Local Polynomial Regression
研究了将分箱方法扩展到快速计算局部多项式平滑中的辅助量，如自由度、交叉验证函数、方差估计和误差度量，计算量与分箱局部多项式平滑相当。
Abstract We investigate the extension of binning methodology to fast computation of several auxiliary quantities that arise in local polynomial smoothing. Examples include degrees of freedom measures, cross-validation functions, variance estimates, and exact measures of error. It is shown that the computational effort required for such approximations is of the same order of magnitude as that required for a binned local polynomial smooth. Key Words: BinningCross-validationError degrees of freedomKernel estimatorLinear smootherMean average squared errorSmoother matrixStandard error