Use of segmented linear regression under a bayesian approach to detect climate change in different regions of the world (2023)
- Authors:
- USP affiliated authors: ACHCAR, JORGE ALBERTO - FMRP ; BARILI, EMERSON - FMRP
- Unidade: FMRP
- DOI: 10.33140/BSCR
- Subjects: MUDANÇA CLIMÁTICA; REGRESSÃO LINEAR; PRECIPITAÇÃO ATMOSFÉRICA
- Keywords: Climate Data; Segmented Linear Regression Models; Change-Points; Annual Temperature and Precipitation Average
- Language: Inglês
- Imprenta:
- Source:
- Título: Biomedical Science and Clinical Research
- ISSN: 2835-7914
- Volume/Número/Paginação/Ano: v. 2, n. 1, p. 149-158, 2023
- Status:
- Artigo aberto em periódico híbrido (Hybrid Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
-
ABNT
BARILI, Emerson e ACHCAR, Jorge Alberto. Use of segmented linear regression under a bayesian approach to detect climate change in different regions of the world. Biomedical Science and Clinical Research, v. 2, n. 1, p. 149-158, 2023Tradução . . Disponível em: https://doi.org/10.33140/BSCR. Acesso em: 08 abr. 2026. -
APA
Barili, E., & Achcar, J. A. (2023). Use of segmented linear regression under a bayesian approach to detect climate change in different regions of the world. Biomedical Science and Clinical Research, 2( 1), 149-158. doi:10.33140/BSCR -
NLM
Barili E, Achcar JA. Use of segmented linear regression under a bayesian approach to detect climate change in different regions of the world [Internet]. Biomedical Science and Clinical Research. 2023 ; 2( 1): 149-158.[citado 2026 abr. 08 ] Available from: https://doi.org/10.33140/BSCR -
Vancouver
Barili E, Achcar JA. Use of segmented linear regression under a bayesian approach to detect climate change in different regions of the world [Internet]. Biomedical Science and Clinical Research. 2023 ; 2( 1): 149-158.[citado 2026 abr. 08 ] Available from: https://doi.org/10.33140/BSCR - Semiparametric transformation model in presence of cure fraction: a hierarchical Bayesian approach assuming the unknown hazards as latent factors
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