2009
DOI: 10.1080/00207160902783557
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Abstract: In this work, the application of 'multivariate adaptive regression splines' (MARS) for modelling osteoporosis is described. This article focuses on the explanation of a new technique that combines the use of the principal components analysis (PCA) method with MARS. The use of this new technique allows for an easier management of large databases with a lower computational cost as the PCA allows the elimination of those variables that are redundant from the point of view of the phenomena under study. Osteoporosi… Show more

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Cited by 39 publications
(26 citation statements)
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“…These will be denoted by the small letter t. For a spline of degree q each segment is a polynomial function. MARS uses two-sided truncated power functions as spline basis functions, described by the following equations [22][23][24]27,[35][36][37][38][39][40]:…”
Section: Multivariate Adaptive Regression Splines (Mars)mentioning
confidence: 99%
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“…These will be denoted by the small letter t. For a spline of degree q each segment is a polynomial function. MARS uses two-sided truncated power functions as spline basis functions, described by the following equations [22][23][24]27,[35][36][37][38][39][40]:…”
Section: Multivariate Adaptive Regression Splines (Mars)mentioning
confidence: 99%
“…First, in order to select the consecutive pairs of basis functions of the model, a two-at-a-time forward stepwise procedure is implemented [22][23][24]27,[35][36][37][38][39][40]. This forward stepwise selection of basis function leads to a very complex and overfitted model.…”
Section:  mentioning
confidence: 99%
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