Multivariate Adaptive Regression Splines (Preprint)

Abstract

A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data. This procedure is motivated by the recursive partitioning approach to regression and shares its attractive properties. Unlike recursive partitioning, however this method produces continuous models with continuous derivatives. It has more power and flexibility to model relationships that are nearly additive or involve interactions in at most a few variables. In addition, the model can be represented in a form that separately identifies the additive contributions and those associated with the different multivariable interactions.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1990
Accession Number
ADA578458

Entities

People

  • Jerome H. Friedman

Organizations

  • Stanford University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Alkenes
  • Chemistry
  • Computational Science
  • Computer Science
  • Data Analysis
  • Data Mining
  • Data Science
  • Databases
  • Fatty Acids
  • Information Science
  • Linear Accelerators
  • Mathematics
  • Network Science
  • Statistical Algorithms
  • Statistics
  • Surveys

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Approximation Theory.