Flexible regression modelling under shape constraints

Andrew A. Manderson, Kevin Murray, Berwin A. Turlach

Research output: Chapter in Book/Conference paperChapterpeer-review

Abstract

This chapter presents methodology for estimating the posterior distribution of regression models subject to shape constraints. There is a specific focus on estimating and performing covariate selection for monotonic polynomials, however the computational and methodological techniques are more widely applicable. We demonstrate our methodology on a number of simulated and real-world data sets. Our covariate selection code is available as an R package at https://github.com/hhau/rjmonopoly.

Original languageEnglish
Title of host publicationFlexible Bayesian Regression Modelling
EditorsYanan Fan, David Nott, Michael S. Smith, Jean-Luc Dortet-Bernadet
PublisherAcademic Press
Chapter9
Pages251-279
Number of pages29
ISBN (Electronic)9780128158630
ISBN (Print)9780128158623
DOIs
Publication statusPublished - 2020

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