The new odd log-logistic generalized inverse Gaussian regression model
type: Item
instance of: scholarly article
title: The new odd log-logistic generalized inverse Gaussian regression model
zbMATH Open document ID: 1431.62318
DOI: 10.1155/2019/8575424
author: Julio Cezar Souza Vasconcelos
author: Edwin M. M. Ortega
author: Elton G. Araújo
author: Gauss M. Cordeiro
published in: Journal of Probability and Statistics
publication date: 2019-03-21T00:00:00Z
review text: Summary: We define a new four-parameter model called the odd log-logistic generalized inverse Gaussian distribution which extends the generalized inverse Gaussian and inverse Gaussian distributions. We obtain some structural properties of the new distribution. We construct an extended regression model based on this distribution with two systematic structures, which can provide more realistic fits to real data than other special regression models. We adopt the method of maximum likelihood to estimate the model parameters. In addition, various simulations are performed for different parameter settings and sample sizes to check the accuracy of the maximum likelihood estimators. We provide a diagnostics analysis based on case-deletion and quantile residuals. Finally, the potentiality of the new regression model to predict price of urban property is illustrated by means of real data.
Mathematics Subject Classification ID: 62J12
Mathematics Subject Classification ID: 60E05
Mathematics Subject Classification ID: 62J20
Mathematics Subject Classification ID: 62P20
zbMATH DE Number: 7039896
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2019/8575424
OpenAlex ID: W2908887289
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description: scientific article; zbMATH DE number 7039896