Regression Models

Michael Zabarankin and Stan Uryasev
Additional contact information
Michael Zabarankin: Stevens Institute of Technology
Stan Uryasev: University of Florida

Chapter Chapter 6 in Statistical Decision Problems, 2014, pp 71-87 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-1-4614-8471-4_6

Ordering information: This item can be ordered from
http://www.springer.com/9781461484714

DOI: 10.1007/978-1-4614-8471-4_6

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().