Optimal burn-in policy based on a set of cutoff points using mixture inverse Gaussian degradation process and copulas (2021)
- Authors:
- USP affiliated authors: LOUZADA NETO, FRANCISCO - ICMC ; RAMOS, PEDRO LUIZ - ICMC
- Unidade: ICMC
- DOI: 10.1002/asmb.2601
- Subjects: INFERÊNCIA BAYESIANA; PROCESSOS GAUSSIANOS
- Keywords: burn-in test; copula; LASER degradation data; mixture inverse Gaussian process; optimal cutoff points; optimal termination time
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Applied Stochastic Models in Business and Industry
- ISSN: 1524-1904
- Volume/Número/Paginação/Ano: v. 37, n. 3, p. 612-627, May-June 2021
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
MORITA, Lia Hanna Martins et al. Optimal burn-in policy based on a set of cutoff points using mixture inverse Gaussian degradation process and copulas. Applied Stochastic Models in Business and Industry, v. 37, n. 3, p. 612-627, 2021Tradução . . Disponível em: https://doi.org/10.1002/asmb.2601. Acesso em: 10 fev. 2026. -
APA
Morita, L. H. M., Tomazella, V. L. D., Ferreira, P. H., Ramos, P. L., Balakrishnan, N., & Louzada, F. (2021). Optimal burn-in policy based on a set of cutoff points using mixture inverse Gaussian degradation process and copulas. Applied Stochastic Models in Business and Industry, 37( 3), 612-627. doi:10.1002/asmb.2601 -
NLM
Morita LHM, Tomazella VLD, Ferreira PH, Ramos PL, Balakrishnan N, Louzada F. Optimal burn-in policy based on a set of cutoff points using mixture inverse Gaussian degradation process and copulas [Internet]. Applied Stochastic Models in Business and Industry. 2021 ; 37( 3): 612-627.[citado 2026 fev. 10 ] Available from: https://doi.org/10.1002/asmb.2601 -
Vancouver
Morita LHM, Tomazella VLD, Ferreira PH, Ramos PL, Balakrishnan N, Louzada F. Optimal burn-in policy based on a set of cutoff points using mixture inverse Gaussian degradation process and copulas [Internet]. Applied Stochastic Models in Business and Industry. 2021 ; 37( 3): 612-627.[citado 2026 fev. 10 ] Available from: https://doi.org/10.1002/asmb.2601 - Generalizing normality: different estimation methods for skewed information
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Informações sobre o DOI: 10.1002/asmb.2601 (Fonte: oaDOI API)
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