Power logit regression for modeling bounded data

Autor:	Francisco F. Queiroz, Silvia L. P. Ferrari
Rok vydání:	2023
Předmět:	Statistics and Probability Statistics Probability and Uncertainty
Zdroj:	Statistical Modelling. :1471082X2211401
ISSN:	1477-0342 1471-082X
DOI:	10.1177/1471082x221140157
Popis:	The main purpose of this article 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 article 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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::875c3320aeec8beb43da47f0d216ad78 https://doi.org/10.1177/1471082x221140157 Zobrazit plný text záznamu