A random effect regression based on the odd log-logistic generalized inverse Gaussian distribution.

Autor: Vasconcelos JCS; ESALQ, Universidade de São Paulo, Piracicaba, Brazil., Cordeiro GM; UFPE, Universidade Federal de Pernambuco, Recife, Brazil., Ortega EMM; ESALQ, Universidade de São Paulo, Piracicaba, Brazil., Silva GO; UFBA, Universidade Federal da Bahia, Salvador, Brazil.
Jazyk: angličtina
Zdroj: Journal of applied statistics [J Appl Stat] 2022 Jan 06; Vol. 50 (5), pp. 1199-1214. Date of Electronic Publication: 2022 Jan 06 (Print Publication: 2023).
DOI: 10.1080/02664763.2021.2024515
Abstrakt: In recent decades, the use of regression models with random effects has made great progress. Among these models' attractions is the flexibility to analyze correlated data. In various situations, the distribution of the response variable presents asymmetry or bimodality. In these cases, it is possible to use the normal regression with random effect at the intercept. In light of these contexts, i.e. the desire to analyze correlated data in the presence of bimodality or asymmetry, in this paper we propose a regression model with random effect at the intercept based onthe generalized inverse Gaussian distribution model with correlated data. The maximum likelihood is adopted to estimate the parameters and various simulations are performed for correlated data. A type of residuals for the new regression is proposed whose empirical distribution is close to normal. The versatility of the new regression is demonstrated by estimating the average price per hectare of bare land in 10 municipalities in the state of São Paulo (Brazil). In this context, various databases are constantly emerging, requiring flexible modeling. Thus, it is likely to be of interest to data analysts, and can make a good contribution to the statistical literature.
Competing Interests: No potential conflict of interest was reported by the authors.
(© 2022 Informa UK Limited, trading as Taylor & Francis Group.)
Databáze: MEDLINE
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