dorsal/arxiv
View SchemaFlexible modeling of nonnegative continuous data: Box-Cox symmetric regression and its zero-adjusted extension
| Authors | Rodrigo M. R. de Medeiros, Francisco F. Queiroz |
|---|---|
| Categories | |
| ArXiv ID | 2601.08600vv1 |
| URL | https://arxiv.org/abs/2601.08600 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
The Box-Cox symmetric distributions constitute a broad class of probability models for positive continuous data, offering flexibility in modeling skewness and tail behavior. Their parameterization allows a straightforward quantile-based interpretation, which is particularly useful in regression modeling. Despite their potential, only a few specific distributions within this class have been explored in regression contexts, and zero-adjusted extensions have not yet been formally addressed in the literature. This paper formalizes the class of Box-Cox symmetric regression models and introduces a new zero-adjusted extension suitable for modeling data with a non-negligible proportion of observations equal to zero. We discuss maximum likelihood estimation, assess finite-sample performance through simulations, and develop diagnostic tools including residual analysis, local influence measures, and goodness-of-fit statistics. An empirical application on basic education expenditure illustrates the models' ability to capture complex patterns in zero-inflated and highly skewed nonnegative data. To support practical use, we developed the new BCSreg R package, which implements all proposed methods.
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"abstract": "The Box-Cox symmetric distributions constitute a broad class of probability models for positive continuous data, offering flexibility in modeling skewness and tail behavior. Their parameterization allows a straightforward quantile-based interpretation, which is particularly useful in regression modeling. Despite their potential, only a few specific distributions within this class have been explored in regression contexts, and zero-adjusted extensions have not yet been formally addressed in the literature. This paper formalizes the class of Box-Cox symmetric regression models and introduces a new zero-adjusted extension suitable for modeling data with a non-negligible proportion of observations equal to zero. We discuss maximum likelihood estimation, assess finite-sample performance through simulations, and develop diagnostic tools including residual analysis, local influence measures, and goodness-of-fit statistics. An empirical application on basic education expenditure illustrates the models\u0027 ability to capture complex patterns in zero-inflated and highly skewed nonnegative data. To support practical use, we developed the new BCSreg R package, which implements all proposed methods.",
"arxiv_id": "2601.08600",
"authors": [
"Rodrigo M. R. de Medeiros",
"Francisco F. Queiroz"
],
"categories": [
"stat.ME"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Flexible modeling of nonnegative continuous data: Box-Cox symmetric regression and its zero-adjusted extension",
"url": "https://arxiv.org/abs/2601.08600",
"version": "v1"
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