Power logit regression for modeling bounded data
Methodology
2026-05-15 v1
Abstract
The main purpose of this paper is to introduce a new class of regression models for bounded continuous data, commonly encountered in applied research. The models, named the power logit regression models, assume that the response variable follows a distribution in a wide, flexible class of distributions with three parameters, namely the median, a dispersion parameter and a skewness parameter. The paper offers a comprehensive set of tools for likelihood inference and diagnostic analysis, and introduces the new R package PLreg. Applications with real and simulated data show the merits of the proposed models, the statistical tools, and the computational package.
Cite
@article{arxiv.2202.01697,
title = {Power logit regression for modeling bounded data},
author = {Francisco Felipe Queiroz and Silvia Lopes Paula Ferrari},
journal= {arXiv preprint arXiv:2202.01697},
year = {2026}
}