Abstract

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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