期刊名称：International Journal of Statistics and Probability
印刷版ISSN：1927-7032
电子版ISSN：1927-7040
出版年度：2014
卷号：3
期号：3
页码：96
DOI：10.5539/ijsp.v3n3p96
出版社：Canadian Center of Science and Education
摘要：Penalized regression splines are a commonly used method to estimate complex non-linear relationships between two variables. The fit of a penalized regression spline to the data depends on the number of knots, knot placement, and the value of the smoothing parameter. In this paper, we use a simulation study to compare knot selection methods with equidistant knots in a penalized regression spline model. We found that one method generally performed better than others. The results provide guidance in selecting the number of equidistant knots in a penalized regression spline model.
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