Confidence intervals for the risks of regression models

Imhoi Koo, Rhee Man Kil

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The empirical risks of regression models are not accurate since they are evaluated from the finite number of samples. In this context, we investigate the confidence intervals for the risks of regression models, that is, the intervals between the expected and empirical risks. The suggested method of estimating confidence intervals can provide a tool for predicting the performance of regression models.

Original languageEnglish (US)
Title of host publicationNeural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
PublisherSpringer Verlag
Pages755-764
Number of pages10
ISBN (Print)3540464794, 9783540464792
StatePublished - Jan 1 2006
Event13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, China
Duration: Oct 3 2006Oct 6 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4232 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Neural Information Processing, ICONIP 2006
CountryChina
CityHong Kong
Period10/3/0610/6/06

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Koo, I., & Kil, R. M. (2006). Confidence intervals for the risks of regression models. In Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings (pp. 755-764). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4232 LNCS). Springer Verlag.