Testing correct model specification using extreme learning machines

Jin Seo Cho, Halbert White

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Testing the correct model specification hypothesis for artificial neural network (ANN) models of the conditional mean is not standard. The traditional Wald, Lagrange multiplier, and quasi-likelihood ratio statistics weakly converge to functions of Gaussian processes, rather than to convenient chi-squared distributions. Also, their large-sample null distributions are problem dependent, limiting applicability. We overcome this challenge by applying functional regression methods of Cho et al. [8] to extreme learning machines (ELM). The Wald ELM (WELM) test statistic proposed here is easy to compute and has a large-sample standard chi-squared distribution under the null hypothesis of correct specification. We provide associated theory for time-series data and affirm our theory with some Monte Carlo experiments.

Original languageEnglish
Pages (from-to)2552-2565
Number of pages14
Issue number16
Publication statusPublished - 2011 Sep

Bibliographical note

Funding Information:
Cho acknowledges research support from the Korea Sanhak foundation.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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