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 |
|---|---|
| Pages (from-to) | 2552-2565 |
| Number of pages | 14 |
| Journal | Neurocomputing |
| Volume | 74 |
| Issue number | 16 |
| DOIs | |
| Publication status | Published - 2011 Sep 1 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence
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Testing correct model specification using extreme learning machines. / Cho, Jin Seo; White, Halbert.
In: Neurocomputing, Vol. 74, No. 16, 01.09.2011, p. 2552-2565.Research output: Contribution to journal › Article
TY - JOUR
T1 - Testing correct model specification using extreme learning machines
AU - Cho, Jin Seo
AU - White, Halbert
PY - 2011/9/1
Y1 - 2011/9/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80053626716&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053626716&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2010.11.031
DO - 10.1016/j.neucom.2010.11.031
M3 - Article
AN - SCOPUS:80053626716
VL - 74
SP - 2552
EP - 2565
JO - Review of Economic Dynamics
JF - Review of Economic Dynamics
SN - 1094-2025
IS - 16
ER -