Partitioned cross-validation
提出分区交叉验证方法，以降低普通交叉验证的样本间变异性，并引入偏差以平衡方差，给出最优权衡理论，与其他带宽选择方法比较。
Partitioned cross-validation is proposed as a method for overcoming the large amounts of across sample variability to which ordinary cross-validation is subject. The price for cutting down on the sample noise is that a type of bias is intriduced. A theory is presented for optimal trade-off of this variance and bias. Comparison with other bandwidth selection methods is given.