Unified multivariate survival model with a surviving fraction: an application to a Brazilian customer churn data (2016)
- Authors:
- USP affiliated authors: CANCHO, VICENTE GARIBAY - ICMC ; LOUZADA NETO, FRANCISCO - ICMC
- Unidade: ICMC
- DOI: 10.1080/02664763.2015.1071341
- Subjects: INFERÊNCIA BAYESIANA; ESTATÍSTICA; ESTATÍSTICA APLICADA; REGRESSÃO LINEAR; ANÁLISE DE REGRESSÃO E DE CORRELAÇÃO; INFERÊNCIA ESTATÍSTICA
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Journal of Applied Statistics
- ISSN: 0266-4763
- Volume/Número/Paginação/Ano: v. 43, n. 3, p. 572-584, 2016
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
CANCHO, Vicente Garibay e DEY, Dipak K. e LOUZADA, Francisco. Unified multivariate survival model with a surviving fraction: an application to a Brazilian customer churn data. Journal of Applied Statistics, v. 43, n. 3, p. 572-584, 2016Tradução . . Disponível em: https://doi.org/10.1080/02664763.2015.1071341. Acesso em: 24 abr. 2024. -
APA
Cancho, V. G., Dey, D. K., & Louzada, F. (2016). Unified multivariate survival model with a surviving fraction: an application to a Brazilian customer churn data. Journal of Applied Statistics, 43( 3), 572-584. doi:10.1080/02664763.2015.1071341 -
NLM
Cancho VG, Dey DK, Louzada F. Unified multivariate survival model with a surviving fraction: an application to a Brazilian customer churn data [Internet]. Journal of Applied Statistics. 2016 ; 43( 3): 572-584.[citado 2024 abr. 24 ] Available from: https://doi.org/10.1080/02664763.2015.1071341 -
Vancouver
Cancho VG, Dey DK, Louzada F. Unified multivariate survival model with a surviving fraction: an application to a Brazilian customer churn data [Internet]. Journal of Applied Statistics. 2016 ; 43( 3): 572-584.[citado 2024 abr. 24 ] Available from: https://doi.org/10.1080/02664763.2015.1071341 - The Poisson-exponential model for recurrent event data: an application to bowel motility data
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Informações sobre o DOI: 10.1080/02664763.2015.1071341 (Fonte: oaDOI API)
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