Beta regression model nonlinear in the parameters with additive measurement errors in variables.

We propose in this paper a general class of nonlinear beta regression models with measurement errors. The motivation for proposing this model arose from a real problem we shall discuss here. The application concerns a usual oil refinery process where the main covariate is the concentration of a typi...

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Autores principales:	Daniele de Brito Trindade, Patrícia Leone Espinheira, Klaus Leite Pinto Vasconcellos, Jalmar Manuel Farfán Carrasco, Maria do Carmo Soares de Lima
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c38563fc9bc44265a6e523317052b2b9
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spelling	oai:doaj.org-article:c38563fc9bc44265a6e523317052b2b92021-12-02T20:08:59ZBeta regression model nonlinear in the parameters with additive measurement errors in variables.1932-620310.1371/journal.pone.0254103https://doaj.org/article/c38563fc9bc44265a6e523317052b2b92021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254103https://doaj.org/toc/1932-6203We propose in this paper a general class of nonlinear beta regression models with measurement errors. The motivation for proposing this model arose from a real problem we shall discuss here. The application concerns a usual oil refinery process where the main covariate is the concentration of a typically measured in error reagent and the response is a catalyst's percentage of crystallinity involved in the process. Such data have been modeled by nonlinear beta and simplex regression models. Here we propose a nonlinear beta model with the possibility of the chemical reagent concentration being measured with error. The model parameters are estimated by different methods. We perform Monte Carlo simulations aiming to evaluate the performance of point and interval estimators of the model parameters. Both results of simulations and the application favors the method of estimation by maximum pseudo-likelihood approximation.Daniele de Brito TrindadePatrícia Leone EspinheiraKlaus Leite Pinto VasconcellosJalmar Manuel Farfán CarrascoMaria do Carmo Soares de LimaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254103 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daniele de Brito Trindade Patrícia Leone Espinheira Klaus Leite Pinto Vasconcellos Jalmar Manuel Farfán Carrasco Maria do Carmo Soares de Lima Beta regression model nonlinear in the parameters with additive measurement errors in variables.
description	We propose in this paper a general class of nonlinear beta regression models with measurement errors. The motivation for proposing this model arose from a real problem we shall discuss here. The application concerns a usual oil refinery process where the main covariate is the concentration of a typically measured in error reagent and the response is a catalyst's percentage of crystallinity involved in the process. Such data have been modeled by nonlinear beta and simplex regression models. Here we propose a nonlinear beta model with the possibility of the chemical reagent concentration being measured with error. The model parameters are estimated by different methods. We perform Monte Carlo simulations aiming to evaluate the performance of point and interval estimators of the model parameters. Both results of simulations and the application favors the method of estimation by maximum pseudo-likelihood approximation.
format	article
author	Daniele de Brito Trindade Patrícia Leone Espinheira Klaus Leite Pinto Vasconcellos Jalmar Manuel Farfán Carrasco Maria do Carmo Soares de Lima
author_facet	Daniele de Brito Trindade Patrícia Leone Espinheira Klaus Leite Pinto Vasconcellos Jalmar Manuel Farfán Carrasco Maria do Carmo Soares de Lima
author_sort	Daniele de Brito Trindade
title	Beta regression model nonlinear in the parameters with additive measurement errors in variables.
title_short	Beta regression model nonlinear in the parameters with additive measurement errors in variables.
title_full	Beta regression model nonlinear in the parameters with additive measurement errors in variables.
title_fullStr	Beta regression model nonlinear in the parameters with additive measurement errors in variables.
title_full_unstemmed	Beta regression model nonlinear in the parameters with additive measurement errors in variables.
title_sort	beta regression model nonlinear in the parameters with additive measurement errors in variables.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c38563fc9bc44265a6e523317052b2b9
work_keys_str_mv	AT danieledebritotrindade betaregressionmodelnonlinearintheparameterswithadditivemeasurementerrorsinvariables AT patricialeoneespinheira betaregressionmodelnonlinearintheparameterswithadditivemeasurementerrorsinvariables AT klausleitepintovasconcellos betaregressionmodelnonlinearintheparameterswithadditivemeasurementerrorsinvariables AT jalmarmanuelfarfancarrasco betaregressionmodelnonlinearintheparameterswithadditivemeasurementerrorsinvariables AT mariadocarmosoaresdelima betaregressionmodelnonlinearintheparameterswithadditivemeasurementerrorsinvariables
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Beta regression model nonlinear in the parameters with additive measurement errors in variables.

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