Power logit regression for modeling bounded data

TL;DR

This paper introduces power logit regression models for bounded continuous data, offering flexible distribution assumptions, inference tools, diagnostics, and an R package, demonstrated through real and simulated data.

Contribution

It presents a new class of regression models with three parameters for bounded data, along with inference methods, diagnostics, and the R package PLreg.

Findings

Models effectively fit bounded data in applications

The R package PLreg facilitates implementation and diagnostics

Applications demonstrate the models' flexibility and utility

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.