O modelo de regressão GJS inflacionado em zero ou um
Beta regression models are useful for modeling random variables that assume values in the standard unit interval, such as rates and proportions. Such models cannot be used when the data contain zeros and/or ones. In this case, usual regression models, such as normal linear or nonlinear regression mo...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Brasil |
| Institución: | Universidade Federal do Rio Grande do Norte (UFRN) |
| Repositorio: | Repositório Institucional da UFRN |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufrn.br:123456789/26152 |
| Acceso en línea: | https://repositorio.ufrn.br/jspui/handle/123456789/26152 |
| Access Level: | acceso abierto |
| Palabra clave: | Distribuição GJS Modelo de regressão beta inflacionado Modelo de regressão GJS Regressão beta CNPQ::CIENCIAS EXATAS E DA TERRA::MATEMATICA |
| Sumario: | Beta regression models are useful for modeling random variables that assume values in the standard unit interval, such as rates and proportions. Such models cannot be used when the data contain zeros and/or ones. In this case, usual regression models, such as normal linear or nonlinear regression models, are not suitable. The principal aim of this work is to propose a mixed continuous-discrete distributions to model data observed on the intervals [0, 1) or (0, 1] and its associated regression model. The GJS distribution is used to describe the continuous component of the model. The parameters of the mixture distribution are modelled as functions of regression parameters. We study the performance of the maximum likelihood estimators through Monte Carlo simulations. Also, we define a residual for the proposed regression model to assess departures from model assumptions as well as to detect outlying observations, and discuss some influence methods such as the local influence. Finally, applications to real data are presented to show the usefulness of the new regression model. |
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