Electronic Journal of Statistics

A note on least squares sensitivity in single-index model estimation and the benefits of response transformations

Alexandra L. Garnham and Luke A. Prendergast

Full-text: Open access

Abstract

Ordinary Least Squares (OLS) is recognised as being useful in the context of multiple linear regression but can also be effective under the more general framework of the single-index model. In cases where it is ineffective, transformations to the response can improve performance while still allowing for interpretation on the original scale. In this paper we introduce an influence diagnostic for OLS that can be used to assess its effectiveness in the general setting and which can also be used following response transformations. These findings are further emphasized and verified via some simulation studies.

Article information

Source
Electron. J. Statist., Volume 7 (2013), 1983-2004.

Dates
First available in Project Euclid: 5 August 2013

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1375708876

Digital Object Identifier
doi:10.1214/13-EJS831

Mathematical Reviews number (MathSciNet)
MR3085015

Zentralblatt MATH identifier
1293.62141

Subjects
Primary: 62J02: General nonlinear regression
Secondary: 62H12: Estimation

Keywords
influence function response discretization log transformation rank transformation single-index model

Citation

Garnham, Alexandra L.; Prendergast, Luke A. A note on least squares sensitivity in single-index model estimation and the benefits of response transformations. Electron. J. Statist. 7 (2013), 1983--2004. doi:10.1214/13-EJS831. https://projecteuclid.org/euclid.ejs/1375708876


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