This study examines the capability of the Relevance Vector Machine (RVM) and Multivariate Adaptive Regression Spline (MARS) for prediction of ultimate capacity of driven piles and drilled shafts. RVM is a sparse method for training generalized linear models, while MARS technique is basically an adaptive piece-wise regression approach. In this paper, pile capacity prediction models are developed...

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A Bayesian approach to multivariate adaptive regression spline (MARS) ®tting (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...

Multivariate Adaptive Regression Spline (MARS) is a simple and powerful non-parametric machine learning algorithm that automatizes the selection of non-linear terms in regression models. In this study, we propose using MARS spatial framework to account for potential non-linearities effects Using relatively large data set 17,000 dwellings St. Petersburg (Russia), examine how works. The empirical...

An adaptive spline method for smoothing is proposed which combines features from both regression spline and smoothing spline approaches One of its advantages is the ability to vary the amount of smoothing in response to the inhomogeneous curvature of true functions at di erent locations This method can be applied to many multivariate function estimation problems which is illustrated in this pap...

In this paper, we first examine several volatility models in the literature. We then estimate financial volatility using multivariate adaptive regression splines (MARS) by logarithmic transformation as a preliminary analysis to examine a nonparametric volatility model. Despite its popularity, MARS has never been applied to model financial volatility. To implement the MARS methodology in a time ...