Sequentially Estimating the Approximate Conditional Mean Using Extreme Learning Machines.
TL;DR: This study examined the extreme learning machine (ELM) applied to the Wald test statistic for the model specification of the conditional mean and applied the WELM testing procedure to the sequential testing procedure formed by a set of polynomial models and estimate an approximate conditional expectation.
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Abstract: This study examined the extreme learning machine (ELM) applied to the Wald test statistic for the model specification of the conditional mean, which we call the WELM testing procedure. The omnibus test statistics available in the literature weakly converge to a Gaussian stochastic process under the null that the model is correct, and this makes their application inconvenient. By contrast, the WELM testing procedure is straightforwardly applicable when detecting model misspecification. We applied the WELM testing procedure to the sequential testing procedure formed by a set of polynomial models and estimate an approximate conditional expectation. We then conducted extensive Monte Carlo experiments to evaluate the performance of the sequential WELM testing procedure and verify that it consistently estimates the most parsimonious conditional mean when the set of polynomial models contains a correctly specified model. Otherwise, it consistently rejects all the models in the set.
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References
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
23.1K
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TL;DR: A new learning algorithm called ELM is proposed for feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs which tends to provide good generalization performance at extremely fast learning speed.
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Tests for Specification Errors in Classical Linear Least-Squares Regression Analysis
TL;DR: In this article, the authors derived the distributions of the least-squares residuals under a variety of specification errors, including omitted variables, incorrect functional form, simultaneous equation problems and heteroskedasticity.
3.2K
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TL;DR: In this paper, the asymptotic distribution of standard test statistics is described as functionals of chi-square processes, and a transformation based upon a conditional probability measure yields an asymptic distribution free of nuisance parameters, which can be easily approximated via simulation.