Testing for neglected nonlinearity using extreme learning machines

Kyulee Shin, Jin Seo Cho

Research output: Contribution to journalArticlepeer-review

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

Bibliographical note

Funding Information:
The authors are most grateful to the editors and three anonymous referees. We are also benefited from discussions with Halbert White and the participants of ELM2012 held at Singapore. Jin Seo Cho acknowledges research support from the Korea Sanhak Foundation.

All Science Journal Classification (ASJC) codes

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

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