A class of beta regression models with multiplicative log-normal measurement errors (2018)
- Authors:
- Autor USP: ANDRADE FILHO, MÁRIO DE CASTRO - ICMC
- Unidade: ICMC
- DOI: 10.1080/03610918.2017.1280165
- Assunto: REGRESSÃO
- Keywords: Beta regression models; Calibration regression and pseudo likelihood; Errors-in-variables models
- Language: Inglês
- Imprenta:
- Publisher place: Philadelphia
- Date published: 2018
- Source:
- Título do periódico: Communications in Statistics - Simulation and Computation
- ISSN: 0361-0918
- Volume/Número/Paginação/Ano: v. 47, n. 1, p. 229-248, 2018
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
SILVA, Eveliny Barroso da et al. A class of beta regression models with multiplicative log-normal measurement errors. Communications in Statistics - Simulation and Computation, v. 47, n. 1, p. 229-248, 2018Tradução . . Disponível em: https://doi.org/10.1080/03610918.2017.1280165. Acesso em: 24 abr. 2024. -
APA
Silva, E. B. da, Diniz, C. A. R., Carrasco, J. M. F., & Castro, M. de. (2018). A class of beta regression models with multiplicative log-normal measurement errors. Communications in Statistics - Simulation and Computation, 47( 1), 229-248. doi:10.1080/03610918.2017.1280165 -
NLM
Silva EB da, Diniz CAR, Carrasco JMF, Castro M de. A class of beta regression models with multiplicative log-normal measurement errors [Internet]. Communications in Statistics - Simulation and Computation. 2018 ; 47( 1): 229-248.[citado 2024 abr. 24 ] Available from: https://doi.org/10.1080/03610918.2017.1280165 -
Vancouver
Silva EB da, Diniz CAR, Carrasco JMF, Castro M de. A class of beta regression models with multiplicative log-normal measurement errors [Internet]. Communications in Statistics - Simulation and Computation. 2018 ; 47( 1): 229-248.[citado 2024 abr. 24 ] Available from: https://doi.org/10.1080/03610918.2017.1280165 - Influence assessment in an heteroscedastic errors-in-variables model
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Informações sobre o DOI: 10.1080/03610918.2017.1280165 (Fonte: oaDOI API)
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