Abstract
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 language | English |
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Pages (from-to) | 2552-2565 |
Number of pages | 14 |
Journal | Neurocomputing |
Volume | 74 |
Issue number | 16 |
DOIs | |
Publication status | Published - 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