A heteroscedastic measurement error model based on skew and heavy-tailed distributions with known error variances (2018)
- Authors:
- Autor USP: ANDRADE FILHO, MÁRIO DE CASTRO - ICMC
- Unidade: ICMC
- DOI: 10.1080/00949655.2018.1452925
- Subjects: INFERÊNCIA BAYESIANA; ESTATÍSTICA APLICADA; INFERÊNCIA ESTATÍSTICA
- Keywords: ECM algorithm; errors-in-variables model; heteroscedastic errors; maximum likelihood; skew-t distribution
- Language: Inglês
- Imprenta:
- Source:
- Título: Journal of Statistical Computation and Simulation
- ISSN: 0094-9655
- Volume/Número/Paginação/Ano: v. 88, n. 11, p. 2185-2200, Mar. 2018
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
TOMAYA, Lorena Cáceres e CASTRO, Mário de. A heteroscedastic measurement error model based on skew and heavy-tailed distributions with known error variances. Journal of Statistical Computation and Simulation, v. 88, n. 11, p. 2185-2200, 2018Tradução . . Disponível em: https://doi.org/10.1080/00949655.2018.1452925. Acesso em: 10 fev. 2026. -
APA
Tomaya, L. C., & Castro, M. de. (2018). A heteroscedastic measurement error model based on skew and heavy-tailed distributions with known error variances. Journal of Statistical Computation and Simulation, 88( 11), 2185-2200. doi:10.1080/00949655.2018.1452925 -
NLM
Tomaya LC, Castro M de. A heteroscedastic measurement error model based on skew and heavy-tailed distributions with known error variances [Internet]. Journal of Statistical Computation and Simulation. 2018 ; 88( 11): 2185-2200.[citado 2026 fev. 10 ] Available from: https://doi.org/10.1080/00949655.2018.1452925 -
Vancouver
Tomaya LC, Castro M de. A heteroscedastic measurement error model based on skew and heavy-tailed distributions with known error variances [Internet]. Journal of Statistical Computation and Simulation. 2018 ; 88( 11): 2185-2200.[citado 2026 fev. 10 ] Available from: https://doi.org/10.1080/00949655.2018.1452925 - Conditional predictive inference for beta regression model with autoregressive errors
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Informações sobre o DOI: 10.1080/00949655.2018.1452925 (Fonte: oaDOI API)
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