Building flexible regression models: including the Birnbaum-Saunders distribution in the gamlss package
Generalized additive models for location, scale and shape (GAMLSS) are a very flexible statistical modeling framework, being an important generalization of the well-known generalized linear models and generalized additive models. Their main advantage is that any probability distribution (that does n...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Universidade Estadual de Londrina
2021-11-01
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| Series: | Semina: Ciências Exatas e Tecnológicas |
| Subjects: | |
| Online Access: | https://ojs.uel.br/revistas/uel/index.php/semexatas/article/view/44417 |
| _version_ | 1797951688708980736 |
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| author | Fernanda V. Roquim Thiago G. Ramires Luiz R. Nakamura Ana J. Righetto Renato R. Lima Rayne A. Gomes |
| author_facet | Fernanda V. Roquim Thiago G. Ramires Luiz R. Nakamura Ana J. Righetto Renato R. Lima Rayne A. Gomes |
| author_sort | Fernanda V. Roquim |
| collection | DOAJ |
| description | Generalized additive models for location, scale and shape (GAMLSS) are a very flexible statistical modeling framework, being an important generalization of the well-known generalized linear models and generalized additive models. Their main advantage is that any probability distribution (that does not necessarily belong to the exponential family) can be considered to model the response variable and different regression structures can be fitted in each of its parameters. Currently, there are more than 100 distributions that are already implemented in the gamlss package in R software. Nevertheless, researchers can implement different distributions if they are not yet available, e.g., the Birnbaum-Saunders (BS) distribution, which is widely used in fatigue studies. In this paper we make available all codes regarding the inclusion of the BS distribution in the gamlss package, and then present a simple application related to air quality data for illustration purposes |
| first_indexed | 2024-04-10T22:34:33Z |
| format | Article |
| id | doaj.art-e46893a8c70d4fe79bf3546d95777a97 |
| institution | Directory Open Access Journal |
| issn | 1676-5451 1679-0375 |
| language | English |
| last_indexed | 2024-04-10T22:34:33Z |
| publishDate | 2021-11-01 |
| publisher | Universidade Estadual de Londrina |
| record_format | Article |
| series | Semina: Ciências Exatas e Tecnológicas |
| spelling | doaj.art-e46893a8c70d4fe79bf3546d95777a972023-01-16T15:54:03ZengUniversidade Estadual de LondrinaSemina: Ciências Exatas e Tecnológicas1676-54511679-03752021-11-0142210.5433/1679-0375.2021v42n2p163Building flexible regression models: including the Birnbaum-Saunders distribution in the gamlss packageFernanda V. Roquim0Thiago G. Ramires1Luiz R. Nakamura2Ana J. Righetto3Renato R. Lima4Rayne A. Gomes5Universidade Federal de Santa Catarina - UFSCUniversidade Tecnológica Federal do Paraná -UTFPRUniversidade Federal de Santa Catarina - UFSCALVAZ - LondrinaUniversidade Federal de Lavras - UFLAUniversidade Tecnológica Federal do Paraná -UTFPRGeneralized additive models for location, scale and shape (GAMLSS) are a very flexible statistical modeling framework, being an important generalization of the well-known generalized linear models and generalized additive models. Their main advantage is that any probability distribution (that does not necessarily belong to the exponential family) can be considered to model the response variable and different regression structures can be fitted in each of its parameters. Currently, there are more than 100 distributions that are already implemented in the gamlss package in R software. Nevertheless, researchers can implement different distributions if they are not yet available, e.g., the Birnbaum-Saunders (BS) distribution, which is widely used in fatigue studies. In this paper we make available all codes regarding the inclusion of the BS distribution in the gamlss package, and then present a simple application related to air quality data for illustration purposeshttps://ojs.uel.br/revistas/uel/index.php/semexatas/article/view/44417Smoothing functionsStatistical modelingGeneralized additive modelsGeneralized linear models |
| spellingShingle | Fernanda V. Roquim Thiago G. Ramires Luiz R. Nakamura Ana J. Righetto Renato R. Lima Rayne A. Gomes Building flexible regression models: including the Birnbaum-Saunders distribution in the gamlss package Semina: Ciências Exatas e Tecnológicas Smoothing functions Statistical modeling Generalized additive models Generalized linear models |
| title | Building flexible regression models: including the Birnbaum-Saunders distribution in the gamlss package |
| title_full | Building flexible regression models: including the Birnbaum-Saunders distribution in the gamlss package |
| title_fullStr | Building flexible regression models: including the Birnbaum-Saunders distribution in the gamlss package |
| title_full_unstemmed | Building flexible regression models: including the Birnbaum-Saunders distribution in the gamlss package |
| title_short | Building flexible regression models: including the Birnbaum-Saunders distribution in the gamlss package |
| title_sort | building flexible regression models including the birnbaum saunders distribution in the gamlss package |
| topic | Smoothing functions Statistical modeling Generalized additive models Generalized linear models |
| url | https://ojs.uel.br/revistas/uel/index.php/semexatas/article/view/44417 |
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