Segal M R, Weiss S T, Speizer F E, Tager I B
Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115.
Stat Med. 1988 May;7(5):601-11. doi: 10.1002/sim.4780070507.
The development of techniques for fitting non-parametric smooth curves has resulted in less restrictive regression models. We discuss the ideas underlying such smoothing algorithms, develop their application to epidemiologic studies and address specific issues, such as coping with correlated errors. An example illustrates a particular smoothing approach, as applied to pulmonary function data. The method provides new insight into the effect of smoking on pulmonary function. The discussion offers some qualitative comparisons between smoothing methods and conventional linear models.
用于拟合非参数平滑曲线的技术发展产生了限制较少的回归模型。我们讨论此类平滑算法背后的思想，将其应用于流行病学研究并解决特定问题，如处理相关误差。一个例子说明了一种应用于肺功能数据的特定平滑方法。该方法为吸烟对肺功能的影响提供了新的见解。讨论对平滑方法和传统线性模型进行了一些定性比较。
