Improved estimators in beta prime regression models

• Main Authors: Medeiros, Francisco M. C, Araújo, Mariana C, Bourguignon, Marcelo
• Format: Journal Article
• Language: English
• Published: 26-08-2020
• Subjects: Statistics - Methodology
• DOI: 10.48550/arxiv.2008.11750

In this paper, we consider the beta prime regression model recently proposed
by \cite{bour18}, which is tailored to situations where the response is
continuous and restricted to the positive real line with skewed and long tails
and the regression structure involves regressors and unknown parameters. We
consider two different strategies of bias correction of the maximum-likelihood
estimators for the parameters that index the model. In particular, we discuss
bias-corrected estimators for the mean and the dispersion parameters of the
model. Furthermore, as an alternative to the two analytically bias-corrected
estimators discussed, we consider a bias correction mechanism based on the
parametric bootstrap. The numerical results show that the bias correction
scheme yields nearly unbiased estimates. An example with real data is presented
and discussed.