Geometrically designed, variable knot regression splines

Kaishev, V. K., Dimitrova, D. S., Haberman, S. and Verrall, R. J. (2016). Geometrically designed, variable knot regression splines. Computational Statistics, 31(3), pp. 1079-1105. doi: 10.1007/s00180-015-0621-7

Abstract

A new method of Geometrically Designed least squares (LS) splines with variable knots, named GeDS, is proposed. It is based on the property that the spline regression function, viewed as a parametric curve, has a control polygon and, due to the shape preserving and convex hull properties, it closely follows the shape of this control polygon. The latter has vertices whose x-coordinates are certain knot averages and whose y-coordinates are the regression coefficients. Thus, manipulation of the position of the control polygon may be interpreted as estimation of the spline curve knots and coefficients. These geometric ideas are implemented in the two stages of the GeDS estimation method. In stage A, a linear LS spline fit to the data is constructed, and viewed as the initial position of the control polygon of a higher order (n > 2) smooth spline curve. In stage B, the optimal set of knots of this higher order spline curve is found, so that its control polygon is as close to the initial polygon of stage A as possible and finally, the LS estimates of the regression coefficients of this curve are found. The GeDS method produces simultaneously linear, quadratic, cubic (and possibly higher order) spline fits with one and the same number of B-spline coefficients. Numerical examples are provided and further supplemental materials are available online.

Publication Type: Article The final publication will be available at http://dx.doi.org/10.1007/s00180-015-0621-7 spline regression, B-splines, Greville abscissae, variable knot splines, control polygon Q Science > QA Mathematics Cass Business School > Actuarial Science & Insurance http://openaccess.city.ac.uk/id/eprint/12418
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