A Bayesian goodness-of-fit test for regression

Andrés F. Barrientos and Antonio Canale

Computational Statistics & Data Analysis, 2021, vol. 155, issue C

Abstract: Regression models are widely used statistical procedures, and the validation of their assumptions plays a crucial role in the data analysis process. Unfortunately, validating assumptions usually depends on the availability of tests tailored to the specific model of interest. A novel Bayesian goodness-of-fit hypothesis testing approach is presented for a broad class of regression models the response variable of which is univariate and continuous. The proposed approach relies on a suitable transformation of the response variable and a Bayesian prior induced by a predictor-dependent mixture model. Hypothesis testing is performed via Bayes factor, the asymptotic properties of which are discussed. The method is implemented by means of a Markov chain Monte Carlo algorithm, and its performance is illustrated using simulated and real data sets.

Keywords: Bayes factor; Density regression; Dirichlet process mixture; Rosenblatt’s transformation; Universal residuals (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:155:y:2021:i:c:s016794732030195x

DOI: 10.1016/j.csda.2020.107104

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