A new extended normal quantile regression model: properties and applications

Abstract

We propose the exponentiated odd log-logistic normal quantile regression model relating the covariates to the parameters through two systematic components, and adopt the maximum likelihood method to estimate the parameters. We report Monte Carlo simulations to verify the accuracy of the maximum likelihood estimators. The usefulness of the new regression is proved using three applications to real data. It and its special cases proved to be interesting alternatives to model data in different shapes, and obtain important information on the response variable from the analysis of different quantiles.

Keywords:

• Gamlss
• Herbivorous fungus gnat
• Maximum likelihood
• Odd log-logistic
• Quantile residuals

Acknowledgments

The authors are very grateful to the editor and two referees for helpful comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The financial support from CAPES and CNPq is gratefully acknowledged.