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Predictive performance of penalized beta regression model for continuous bounded outcomes

Ogundimu, Emmanuel O.; Collins, Gary S.

Predictive performance of penalized beta regression model for continuous bounded outcomes Thumbnail


Authors

Gary S. Collins



Abstract

Prediction models for continuous bounded outcomes are often developed by fitting ordinary least-square regression. However, predicted values from such method may lie outside the range of the outcome as it is bounded within a fixed range, with nonlinear expectation due to the ceiling and floor effects of the bounds. Thus, regular regression models such as normal linear or nonlinear models, are inadequate for prediction purposes for bounded response variable and the use of distributions that can model different shapes are essential. Beta regression, apart from modeling different shapes and constraining predictions to an admissible range, has been shown to be superior to alternative methods for data fitting but not for prediction purposes. We take data structures into account and compared various penalized beta regression method on predictive accuracy for bounded outcome variables using optimism corrected measures. Contrary to results obtained under many regression contexts, the classical maximum likelihood method produced good predictive accuracy in terms of R2 and RMSE. The ridge penalized beta regression performed better in terms of g-index, which is a measure of performance of the methods in external data sets. We restricted attention to prespecified models throughout and as such variable selection methods are not evaluated.

Citation

Ogundimu, E. O., & Collins, G. S. (2018). Predictive performance of penalized beta regression model for continuous bounded outcomes. Journal of Applied Statistics, 45(6), 1030-1040. https://doi.org/10.1080/02664763.2017.1339024

Journal Article Type Article
Acceptance Date Apr 30, 2017
Online Publication Date Jun 13, 2017
Publication Date 2018
Deposit Date Oct 11, 2020
Publicly Available Date Oct 15, 2021
Journal Journal of Applied Statistics
Print ISSN 0266-4763
Electronic ISSN 1360-0532
Publisher Taylor and Francis Group
Peer Reviewed Peer Reviewed
Volume 45
Issue 6
Pages 1030-1040
DOI https://doi.org/10.1080/02664763.2017.1339024
Public URL https://durham-repository.worktribe.com/output/1254368

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Accepted Journal Article (721 Kb)
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Publisher Licence URL
http://creativecommons.org/licenses/by-nc/4.0/

Copyright Statement
“This is an Accepted Manuscript version of the following article, accepted for publication in Journal of Applied Statistics. Ogundimu, Emmanuel O. & Collins, Gary S. (2018). Predictive performance of penalized beta regression model for continuous bounded outcomes. Journal of Applied Statistics 45(6): 1030-1040.. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.”





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