JSM 2005 - Toronto

Abstract #304499

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 268
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #304499
Title: Tutorial on Regression Splines
Author(s): Jill McCracken*+ and Pier Bobys and Yasmin Said and Carlos Alzola
Companies: Booz Allen Hamilton and Booz Allen Hamilton and George Mason University and
Address: 9700 Counsellor Drive, Vienna, VA, 22181, United States
Keywords: regression splines
Abstract:

Rather than a simple linear relationship between variables, regression splines generalize the relationship using a piecewise polynomial function. The simplest spline function is the linear spline function with line segments connected at knot locations. Cubic spline functions often are used to approximate relationships because they are smooth and can accommodate more complex relationships in the data. Splines can be applied readily in a regression setting---once the form of the splines, the number of knots, and the knot locations are specified, regression coefficients can be estimated using standard techniques. This paper presents examples of spline models applied to the Boston Housing data, which was the basis for a 1978 paper by Harrison and Rubinfeld on approaches for using housing market data to estimate the willingness to pay for clean air. In this paper, we compare models using ordinary least squares (OLS), linear splines, cubic splines, and multivariate adaptive regression splines (MARS).

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Revised March 2005