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Testing correct model specification using extreme learning machines
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DOI：10.1016/j.neucom.2010.11.031.png)
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Abstract
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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. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Artificial neural networks
Gaussian process
Functional regression
Extreme learning machines
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