Bayesian flexible beta regression model with functional covariate

Agnese Maria Di Brisco (), Enea Giuseppe Bongiorno, Aldo Goia and Sonia Migliorati
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Agnese Maria Di Brisco: Università del Piemonte Orientale
Enea Giuseppe Bongiorno: Università del Piemonte Orientale
Aldo Goia: Università del Piemonte Orientale
Sonia Migliorati: University of Milano-Bicocca

Computational Statistics, 2023, vol. 38, issue 2, No 4, 623-645

Abstract: Abstract Standard parametric regression models are unsuitable when the aim is to predict a bounded continuous response, such as a proportion/percentage or a rate. A possible solution is the flexible beta regression model which is based on a special mixture of betas designed to cope with (though not limited to) bimodality, heavy tails, and outlying observations. This work introduces such a model in the case of a functional covariate, motivated by a spectrometric analysis on milk specimens. Estimation issues are dealt with through a combination of standard basis expansion and Markov chains Monte Carlo techniques. Specifically, the selection of the most significant coefficients of the expansion is done through Bayesian variable selection methods that take advantage of shrinkage priors. The effectiveness of the proposal is illustrated with simulation studies and the application on spectrometric data.

Keywords: Bayesian variable selection; Beta mixture model; Bounded response; Functional data; MCMC (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00180-022-01240-5

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