TESTING FOR NEGLECTED NONLINEARITY USING EXTREME LEARNING MACHINES
KYULEE SHIN, Jin Seo Cho · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 2013
We introduce a statistic testing for neglected nonlinearity using extreme learning machines and call it ELMNN test. The ELMNN test is very convenient and can be widely applied because it is obtained as a by-product of estimating linear models. For the proposed test statistic, we provide a set of regularity conditions under which it asymptotically follows a chi-squared distribution under the null. We conduct Monte Carlo experiments and examine how it behaves when the sample size is finite. Our experiment shows that the test exhibits the properties desired by our theory.