Garcia Damien
CRCHUM - Research Centre, University of Montreal Hospital, Montreal, Canada.
Comput Stat Data Anal. 2010 Apr 1;54(4):1167-1178. doi: 10.1016/j.csda.2009.09.020.
A fully automated smoothing procedure for uniformly-sampled datasets is described. The algorithm, based on a penalized least squares method, allows fast smoothing of data in one and higher dimensions by means of the discrete cosine transform. Automatic choice of the amount of smoothing is carried out by minimizing the generalized cross-validation score. An iteratively weighted robust version of the algorithm is proposed to deal with occurrences of missing and outlying values. Simplified Matlab codes with typical examples in one to three dimensions are provided. A complete user-friendly Matlab program is also supplied. The proposed algorithm - very fast, automatic, robust and requiring low storage -provides an efficient smoother for numerous applications in the area of data analysis.
本文描述了一种用于均匀采样数据集的全自动平滑程序。该算法基于惩罚最小二乘法，通过离散余弦变换实现一维及更高维度数据的快速平滑。通过最小化广义交叉验证分数来自动选择平滑量。提出了该算法的迭代加权稳健版本，以处理缺失值和异常值的情况。提供了一维到三维典型示例的简化Matlab代码。还提供了一个完整的用户友好型Matlab程序。所提出的算法速度非常快、自动、稳健且存储需求低，为数据分析领域的众多应用提供了一种高效的平滑器。
