Revisiting tests for neglected nonlinearity using artificial neural networks

Jin Seo Cho, Isao Ishida, Halbert White

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Tests for regression neglected nonlinearity based on artificial neural networks (ANNs) have so far been studied by separately analyzing the two ways in which the null of regression linearity can hold. This implies that the asymptotic behavior of general ANN-based tests for neglected nonlinearity is still an open question. Here we analyze a convenient ANN-based quasi-likelihood ratio statistic for testing neglected nonlinearity, paying careful attention to both components of the null.We derive the asymptotic null distribution under each component separately and analyze their interaction. Somewhat remarkably, it turns out that the previously known asymptotic null distribution for the type 1 case still applies, but under somewhat stronger conditions than previously recognized. We present Monte Carlo experiments corroborating our theoretical results and showing that standard methods can yield misleading inference when our new, stronger regularity conditions are violated.

Original languageEnglish
Pages (from-to)1133-1186
Number of pages54
JournalNeural Computation
Volume23
Issue number5
DOIs
Publication statusPublished - 2011 May

All Science Journal Classification (ASJC) codes

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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