Bayesian MARS

Authors:
D. G. T. Denison;B. K. Mallick;A. F. M. Smith
Affiliations:
Department of Mathematics, Imperial College of Science, Technology and Medicine, 180 Queen‘s Gate, London SW7 2BZ, UK;Department of Statistics, Texas A & M University, College Station, TX 77843-3143, USA;Principal, Queen Mary and Westfield College, London, E1 4NS UK
Venue:
Statistics and Computing
Year:
1998

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Abstract

A Bayesian approach to multivariate adaptive regression spline (MARS) fitting (Friedman, 1991) is proposed. This takes the form of a probability distribution over the space of possible MARS models which is explored using reversible jump Markov chain Monte Carlo methods (Green, 1995). The generated sample of MARS models produced is shown to have good predictive power when averaged and allows easy interpretation of the relative importance of predictors to the overall fit.