Diagnosing Nonlinearity With Confidence Envelopes for a Semiparametric Approach to Modeling Bivariate Nonlinear Relations Among Latent Variables

Abstract

During the early phases of research, semiparametric models (SPMs) have the advantage of recovering latent nonlinearity over parametric counterparts. Structural equation mixture models (Bauer, 2005) can be applied as SPMs to flexibly recover and describe the form of the unknown latent relationship with minimal distributional assumptions. This short report extends the work on this SPM (Bauer, 2005; Pek, Losardo & Bauer, 2011) by developing approximate simultaneous confidence bands or confidence envelopes (CEs) to evaluate potential nonlinearity of the unknown latent function. A line-finding algorithm to be used in conjunction with these CEs is also developed as an implementation of an informal test to diagnose nonlinearity. Coverage of the CEs and performance of the algorithm in terms of rates of detecting latent nonlinearity are evaluated by Monte Carlo. Recommendations for the use of these CEs and the algorithm for detecting nonlinearity are suggested.