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Article

Extended Exponential Regression Model: Diagnostics and Application to Mineral Data

1
Departamento de Matemática, Facultad de Ingeniería, Universidad de Atacama, Copiapó 1530000, Chile
2
Departamento de Estatística, Universidade Federal do Amazonas, Manaus-AM 69001-000, Brazil
3
Departamento de Matemáticas, Facultad de Ciencias Básicas, Universidad de Antofagasta, Antofagasta 1240000, Chile
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(12), 2042; https://doi.org/10.3390/sym12122042
Received: 18 October 2020 / Revised: 25 November 2020 / Accepted: 3 December 2020 / Published: 10 December 2020
In this paper, we reparameterized the extended exponential model based on the mean in order to include covariates and facilitate the interpretation of the coefficients. The model is compared with common models defined in the positive line also reparametrized in the mean. Parameter estimation is approached based on the expectation–maximization algorithm. Furthermore, we discuss residuals and influence diagnostic tools. A simulation study for recovered parameters is presented. Finally, an application illustrating the advantages of the model in a real data set is presented. View Full-Text
Keywords: EM algorithm; extended exponential distribution; regression model EM algorithm; extended exponential distribution; regression model
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MDPI and ACS Style

Gómez, Y.M.; Gallardo, D.I.; Leão, J.; Gómez, H.W. Extended Exponential Regression Model: Diagnostics and Application to Mineral Data. Symmetry 2020, 12, 2042. https://doi.org/10.3390/sym12122042

AMA Style

Gómez YM, Gallardo DI, Leão J, Gómez HW. Extended Exponential Regression Model: Diagnostics and Application to Mineral Data. Symmetry. 2020; 12(12):2042. https://doi.org/10.3390/sym12122042

Chicago/Turabian Style

Gómez, Yolanda M., Diego I. Gallardo, Jeremias Leão, and Héctor W. Gómez 2020. "Extended Exponential Regression Model: Diagnostics and Application to Mineral Data" Symmetry 12, no. 12: 2042. https://doi.org/10.3390/sym12122042

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