A Comparison of the Reinsch and Speckman Splines
在非参数回归中比较了Reinsch样条（粗糙惩罚法）和Speckman样条（极小化极大法）在曲线估计和误差方差估计上的表现，发现Reinsch平滑样条在两种任务中均接近最优。
In the context of nonparametric regression, spline smoothers can be used both for the estimation of the curve itself and for the estimation of the error variance. We compare two approaches that have been suggested: the Reinsch spline, i.e. roughness penalty, and the Speckman spline, minimax. The comparison is made on the basis of the summed mean squared error for curve estimation and on the basis of a closely related criterion for variance estimation. We show in both asymptotic and small-sample studies that the Reinsch smoothing spline is close to optimal, both as a curve and as a variance estimator.