The re-parameterized inverse Gaussian regression to model length of stay of COVID-19 patients in the public health care system of Piracicaba, Brazil (2023)
- Authors:
- USP affiliated authors: ORTEGA, EDWIN MOISES MARCOS - ESALQ ; CANCHO, VICENTE GARIBAY - ICMC
- Unidades: ESALQ; ICMC
- DOI: 10.1080/02664763.2022.2036707
- Subjects: ANÁLISE DE REGRESSÃO E DE CORRELAÇÃO; COVID-19; DADOS CENSURADOS; MODELOS MATEMÁTICOS; SISTEMA ÚNICO DE SAÚDE
- Keywords: inverse Gaussian distribution; SARS-COV-2
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Journal of Applied Statistics
- ISSN: 0266-4763
- Volume/Número/Paginação/Ano: v. 50, n. 8, p. 1665-1685, 2023
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: green
-
ABNT
HASHIMOTO, Elisabeth Mie et al. The re-parameterized inverse Gaussian regression to model length of stay of COVID-19 patients in the public health care system of Piracicaba, Brazil. Journal of Applied Statistics, v. 50, n. 8, p. 1665-1685, 2023Tradução . . Disponível em: https://doi.org/10.1080/02664763.2022.2036707. Acesso em: 15 maio 2024. -
APA
Hashimoto, E. M., Ortega, E. M. M., Cordeiro, G. M., Cancho, V. G., & Silva, I. (2023). The re-parameterized inverse Gaussian regression to model length of stay of COVID-19 patients in the public health care system of Piracicaba, Brazil. Journal of Applied Statistics, 50( 8), 1665-1685. doi:10.1080/02664763.2022.2036707 -
NLM
Hashimoto EM, Ortega EMM, Cordeiro GM, Cancho VG, Silva I. The re-parameterized inverse Gaussian regression to model length of stay of COVID-19 patients in the public health care system of Piracicaba, Brazil [Internet]. Journal of Applied Statistics. 2023 ; 50( 8): 1665-1685.[citado 2024 maio 15 ] Available from: https://doi.org/10.1080/02664763.2022.2036707 -
Vancouver
Hashimoto EM, Ortega EMM, Cordeiro GM, Cancho VG, Silva I. The re-parameterized inverse Gaussian regression to model length of stay of COVID-19 patients in the public health care system of Piracicaba, Brazil [Internet]. Journal of Applied Statistics. 2023 ; 50( 8): 1665-1685.[citado 2024 maio 15 ] Available from: https://doi.org/10.1080/02664763.2022.2036707 - A power series beta Weibull regression model for predicting breast carcinoma
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Informações sobre o DOI: 10.1080/02664763.2022.2036707 (Fonte: oaDOI API)
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