Projection Pursuit Regression: Some Mathematics.

Abstract

We present some mathematical analysis for a class of curve fitting algorithms labeled 'projection pursuit' algorithms. These algorithms approximate a general function of p variables by a sum of non-linear functions of linear combinations. The approximation is computationally feasible and performs well in examples of nonparametric regression with noisy data, high dimensional density estimation, and multidimensional spline approximation. This note treats the algorithms from the point of view of approximation theory. It is easy to show that approximation is always possible.

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Document Details

Document Type
Technical Report
Publication Date
Apr 07, 1983
Accession Number
ADA134544

Entities

People

  • Mehrdad Shahshahani
  • Persi Diaconis

Organizations

  • Stanford University

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Analytic Functions
  • Computations
  • Curve Fitting
  • Data Analysis
  • Differential Equations
  • Equations
  • Linear Accelerators
  • Mathematical Analysis
  • Monotone Functions
  • New York
  • Numbers
  • Partial Differential Equations
  • Polynomials
  • Real Numbers
  • Theorems
  • United States

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Strategic Security Studies