Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

A novel AIC variant for linear regression models based on a bootstrap correction

Date

Authors

Seghouane, Abd-Krim

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

The Akaike information criterion, AIC, and its corrected version, AIC c are two methods for selecting normal linear regression models. Both criteria were designed as estimators of the expected Kullback-Leibler information between the model generating the

Keywords

Keywords: Akaike Information criterions; Asymptotic approximations; Candidate models; Linear regression models; Polynomial regressions; Simulation results; Small samples; Asymptotic analysis; Learning systems; Linear regression; Polynomial approximation; Regression

URI

http://hdl.handle.net/1885/50143

Collections

ANU Research Publications

Source

Proceedings of IEEE Workshop on Machine Learning for Signal Processing 2008

Type

Conference paper

DOI

10.1109/MLSP.2008.4685469

Restricted until

2037-12-31

Downloads

File

Description

01_Seghouane_A_novel_AIC_variant_for_linear_2008.pdf (199.57 KB)

02_Seghouane_A_novel_AIC_variant_for_linear_2008.pdf (931.56 KB)

03_Seghouane_A_novel_AIC_variant_for_linear_2008.pdf (656.41 KB)

abcd