Bayesian testing of linear versus nonlinear effects using Gaussian process priors

Joris Mulder

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arXiv · 2109.07166

Bayesian testing of linear versus nonlinear effects using Gaussian process priors

Joris Mulder

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Abstract

A Bayes factor is proposed for testing whether the effect of a key predictor variable on the dependent variable is linear or nonlinear, possibly while controlling for certain covariates. The test can be used (i) when one is interested in quantifying the relative evidence in the data of a linear versus a nonlinear relationship and (ii) to quantify the evidence in the data in favor of a linear relationship (useful when building linear models based on transformed variables). Under the nonlinear model, a Gaussian process prior is employed using a parameterization similar to Zellner's $g$ prior resulting in a scale-invariant test. Moreover a Bayes factor is proposed for one-sided testing of whether the nonlinear effect is consistently positive, consistently negative, or neither. Applications are provides from various fields including social network research and education.

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Joris Mulder. 2021-09-15. Bayesian testing of linear versus nonlinear effects using Gaussian process priors. https://arxiv.org/abs/2109.07166

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