A Simulation Study Comparing Knot Selection Methods With Equally Spaced Knots in a Penalized Regression Spline

Eduardo L. Montoya, Nehemias Ulloa, Victoria Miller

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.

Full Text: PDF DOI: 10.5539/ijsp.v3n3p96

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International Journal of Statistics and Probability   ISSN 1927-7032(Print)   ISSN 1927-7040(Online)

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