Testing for neglected nonlinearity using extreme learning machines

Kyulee Shin, Jin Seo Cho

Research output: Contribution to journalArticle

Abstract

We introduce a statistic testing for neglected nonlinearity using extreme learning machines and call it ELMNN test. The ELMNN test is very convenient and can be widely applied because it is obtained as a by-product of estimating linear models. For the proposed test statistic, we provide a set of regularity conditions under which it asymptotically follows a chi-squared distribution under the null. We conduct Monte Carlo experiments and examine how it behaves when the sample size is finite. Our experiment shows that the test exhibits the properties desired by our theory.

Original languageEnglish
Pages (from-to)117-129
Number of pages13
JournalInternational Journal of Uncertainty, Fuzziness and Knowlege-Based Systems
Volume21
Issue numberSUPPL.2
DOIs
Publication statusPublished - 2013 Dec 1

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Testing for neglected nonlinearity using extreme learning machines'. Together they form a unique fingerprint.

  • Cite this