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Regression Models

  • Michael Zabarankin
  • Stan Uryasev
Chapter
  • 1.9k Downloads
Part of the Springer Optimization and Its Applications book series (SOIA, volume 85)

Abstract

In statistics, regression analysis aims to find the best relationship between a response random variable Y (regressant) and n independent variables \(x_{1},\ldots,x_{n}\) (regressors) in the form
$$\displaystyle{Y = f(x_{1},\ldots,x_{n})+\epsilon,}$$
based on m available simultaneous observations of \(x_{1},\ldots,x_{n}\) and Y (regression data), \(x_{1j},\ldots,x_{nj}\), y j , \(j = 1,\ldots,m\), where ε is the approximation error.

Keywords

Quantile Regression Robust Regression Breakdown Point Median Regression Generalize Linear Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, New York 2014

Authors and Affiliations

  • Michael Zabarankin
    • 1
  • Stan Uryasev
    • 2
  1. 1.Department of Mathematical SciencesStevens Institute of TechnologyHobokenUSA
  2. 2.Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA

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