Reparameterized Scale Mixture of Rayleigh Distribution Regression Models with Varying Precision.(2024). Rivera, P.A.; Gallardo, D.I.; Venegas, O.; Gomez-Deniz, E.; Gomez, H.W.

junio 27, 2024
Mathematics

Abstract:

In this paper, we introduce a new parameterization for the scale mixture of the Rayleigh distribution, which uses a mean linear regression model indexed by mean and precision parameters to model asymmetric positive real data. To test the goodness of fit, we introduce two residuals for the new model. A Monte Carlo simulation study is performed to evaluate the parameter estimation of the proposed model. We compare our proposed model with existing alternatives and illustrate its advantages and usefulness using Gilgais data in R software version 4.2.3 with the gamlss package.

Keywords: scale mixture of Rayleigh distribution; maximum likelihood estimator; regression models; residuals

MSC: 62F10; 62E99

DOI:

https://doi.org/10.3390/math12131982

Otras publicaciones

Power Truncated Positive Normal Distribution: A Quantile Regression Approach Applied to Health Databases. (2024). Santoro, K.I.; Gómez, H.J.; Cortés, I.E.; Magalhães, T.M.; Gallardo, D.I.

A Characterization of Concave Mappings Using the Caratheodory Class and Schwarzian Derivative. (2024). Victor; B.; Rodrigo, H.; Venegas, O.

Reparameterized Scale Mixture of Rayleigh Distribution Regression Models with Varying Precision.(2024). Rivera, P.A.; Gallardo, D.I.; Venegas, O.; Gomez-Deniz, E.; Gomez, H.W.

A New Generalization of the Truncated Gumbel Distribution with Quantile Regression and Applications.(2024). Gómez, H.J.; Santoro, K.I.; Ayma, D:; Cortés, I.E.; Gallardo, D.I.; Magalhães, T.M.