In: PLOS ONE
8(4), e61623
[PDF, 1MB]

Abstract
Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1). Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
Item Type: | Journal article |
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Form of publication: | Publisher's Version |
Faculties: | Mathematics, Computer Science and Statistics > Statistics |
Subjects: | 000 Computer science, information and general works > 000 Computer science, knowledge, and systems 500 Science > 570 Life sciences; biology |
URN: | urn:nbn:de:bvb:19-epub-15571-1 |
ISSN: | 1932-6203 |
Language: | English |
Item ID: | 15571 |
Date Deposited: | 14. Jun 2013, 07:05 |
Last Modified: | 04. Nov 2020, 12:56 |