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Artigo de periodico
A bayesian destructive generalized Waring regression cure model with a variance decomposition and application in colorectal cancer data (2024)
Rodrigues, Josemar ; Cancho, Vicente Garibay ; Balakrishnan, Narayanaswamy ; Suzuki, Adriano Kamimura
Fonte: Journal of Statistical Computation and Simulation. Unidade: ICMC
Assuntos: ANÁLISE DE SOBREVIVÊNCIA, SISTEMA IMUNE, NEOPLASIAS COLORRETAIS, FATORES DE RISCO
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ABNT
RODRIGUES, Josemar et al. A bayesian destructive generalized Waring regression cure model with a variance decomposition and application in colorectal cancer data. Journal of Statistical Computation and Simulation, v. 94, n. 14, p. 3111-3130, 2024Tradução . . Disponível em: https://doi.org/10.1080/00949655.2024.2368887. Acesso em: 11 nov. 2024.
APA
Rodrigues, J., Cancho, V. G., Balakrishnan, N., & Suzuki, A. K. (2024). A bayesian destructive generalized Waring regression cure model with a variance decomposition and application in colorectal cancer data. Journal of Statistical Computation and Simulation, 94( 14), 3111-3130. doi:10.1080/00949655.2024.2368887
NLM
Rodrigues J, Cancho VG, Balakrishnan N, Suzuki AK. A bayesian destructive generalized Waring regression cure model with a variance decomposition and application in colorectal cancer data [Internet]. Journal of Statistical Computation and Simulation. 2024 ; 94( 14): 3111-3130.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/00949655.2024.2368887
Vancouver
Rodrigues J, Cancho VG, Balakrishnan N, Suzuki AK. A bayesian destructive generalized Waring regression cure model with a variance decomposition and application in colorectal cancer data [Internet]. Journal of Statistical Computation and Simulation. 2024 ; 94( 14): 3111-3130.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/00949655.2024.2368887
Artigo de periodico
The generalized odd log-logistic-G regression with interval-censored survival data (2024)
Vigas, Valdemiro Piedade ; Ortega, Edwin Moises Marcos ; Suzuki, Adriano Kamimura ; Cordeiro, Gauss Moutinho ; Santos Junior, Paulo Cerqueira
Fonte: Journal of Applied Statistics. Unidades: ESALQ, ICMC
Assuntos: ANÁLISE DE SOBREVIVÊNCIA, DADOS CENSURADOS, INFERÊNCIA BAYESIANA, REGRESSÃO LOGÍSTICA
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VIGAS, Valdemiro Piedade et al. The generalized odd log-logistic-G regression with interval-censored survival data. Journal of Applied Statistics, v. 51, n. 9, p. 2024, 2024Tradução . . Disponível em: https://doi.org/10.1080/02664763.2023.2230533. Acesso em: 11 nov. 2024.
APA
Vigas, V. P., Ortega, E. M. M., Suzuki, A. K., Cordeiro, G. M., & Santos Junior, P. C. (2024). The generalized odd log-logistic-G regression with interval-censored survival data. Journal of Applied Statistics, 51( 9), 2024. doi:10.1080/02664763.2023.2230533
NLM
Vigas VP, Ortega EMM, Suzuki AK, Cordeiro GM, Santos Junior PC. The generalized odd log-logistic-G regression with interval-censored survival data [Internet]. Journal of Applied Statistics. 2024 ; 51( 9): 2024.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/02664763.2023.2230533
Vancouver
Vigas VP, Ortega EMM, Suzuki AK, Cordeiro GM, Santos Junior PC. The generalized odd log-logistic-G regression with interval-censored survival data [Internet]. Journal of Applied Statistics. 2024 ; 51( 9): 2024.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/02664763.2023.2230533
Artigo de periodico
The new Neyman type A generalized odd loglogistic- G-family with cure fraction (2022)
Vigas, Valdemiro Piedade ; Ortega, Edwin Moises Marcos ; Cordeiro, Gauss Moutinho ; Suzuki, Adriano Kamimura ; Silva, Giovana Oliveira
Fonte: Journal of Applied Statistics. Unidades: ESALQ, ICMC
Assuntos: ANÁLISE DE SOBREVIVÊNCIA, DISTRIBUIÇÕES (PROBABILIDADE), MÉTODO DE MONTE CARLO
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ABNT
VIGAS, Valdemiro Piedade et al. The new Neyman type A generalized odd loglogistic- G-family with cure fraction. Journal of Applied Statistics, v. 49, n. 11, p. 2805-2824, 2022Tradução . . Disponível em: https://doi.org/10.1080/02664763.2021.1922994. Acesso em: 11 nov. 2024.
APA
Vigas, V. P., Ortega, E. M. M., Cordeiro, G. M., Suzuki, A. K., & Silva, G. O. (2022). The new Neyman type A generalized odd loglogistic- G-family with cure fraction. Journal of Applied Statistics, 49( 11), 2805-2824. doi:10.1080/02664763.2021.1922994
NLM
Vigas VP, Ortega EMM, Cordeiro GM, Suzuki AK, Silva GO. The new Neyman type A generalized odd loglogistic- G-family with cure fraction [Internet]. Journal of Applied Statistics. 2022 ; 49( 11): 2805-2824.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/02664763.2021.1922994
Vancouver
Vigas VP, Ortega EMM, Cordeiro GM, Suzuki AK, Silva GO. The new Neyman type A generalized odd loglogistic- G-family with cure fraction [Internet]. Journal of Applied Statistics. 2022 ; 49( 11): 2805-2824.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/02664763.2021.1922994
Artigo de periodico
Bayesian survival model induced by frailty for lifetime with long-term survivors (2021)
Cancho, Vicente Garibay ; Barriga, Gladys Dorotea Cacsire ; Cordeiro, Gauss Moutinho ; Ortega, Edwin Moiséis Marcos ; Suzuki, Adriano Kamimura
Fonte: Statistica Neerlandica. Unidades: ICMC, ESALQ
Assuntos: ANÁLISE DE SOBREVIVÊNCIA, INFERÊNCIA BAYESIANA, MODELOS MATEMÁTICOS, NEOPLASIAS COLORRETAIS
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ABNT
CANCHO, Vicente Garibay et al. Bayesian survival model induced by frailty for lifetime with long-term survivors. Statistica Neerlandica, v. 75, n. 3, p. 299-323, 2021Tradução . . Disponível em: https://doi.org/10.1111/stan.12236. Acesso em: 11 nov. 2024.
APA
Cancho, V. G., Barriga, G. D. C., Cordeiro, G. M., Ortega, E. M. M., & Suzuki, A. K. (2021). Bayesian survival model induced by frailty for lifetime with long-term survivors. Statistica Neerlandica, 75( 3), 299-323. doi:10.1111/stan.12236
NLM
Cancho VG, Barriga GDC, Cordeiro GM, Ortega EMM, Suzuki AK. Bayesian survival model induced by frailty for lifetime with long-term survivors [Internet]. Statistica Neerlandica. 2021 ; 75( 3): 299-323.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1111/stan.12236
Vancouver
Cancho VG, Barriga GDC, Cordeiro GM, Ortega EMM, Suzuki AK. Bayesian survival model induced by frailty for lifetime with long-term survivors [Internet]. Statistica Neerlandica. 2021 ; 75( 3): 299-323.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1111/stan.12236
Artigo de periodico
The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis (2020)
Hashimoto, Elisabeth Mie ; Ortega, Edwin Moiséis Marcos ; Cordeiro, Gauss Moutinho ; Suzuki, Adriano Kamimura ; Kattan, Michael W
Fonte: Journal of Applied Statistics. Unidades: ESALQ, ICMC
Assuntos: ANÁLISE DE REGRESSÃO E DE CORRELAÇÃO, DADOS CATEGORIZADOS, INFERÊNCIA BAYESIANA, TRANSPLANTE DE RIM
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ABNT
HASHIMOTO, Elisabeth Mie et al. The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis. Journal of Applied Statistics, v. 47, n. 12, p. 2159–2177, 2020Tradução . . Disponível em: https://doi.org/10.1080/02664763.2019.1706725. Acesso em: 11 nov. 2024.
APA
Hashimoto, E. M., Ortega, E. M. M., Cordeiro, G. M., Suzuki, A. K., & Kattan, M. W. (2020). The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis. Journal of Applied Statistics, 47( 12), 2159–2177. doi:10.1080/02664763.2019.1706725
NLM
Hashimoto EM, Ortega EMM, Cordeiro GM, Suzuki AK, Kattan MW. The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis [Internet]. Journal of Applied Statistics. 2020 ; 47( 12): 2159–2177.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/02664763.2019.1706725
Vancouver
Hashimoto EM, Ortega EMM, Cordeiro GM, Suzuki AK, Kattan MW. The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis [Internet]. Journal of Applied Statistics. 2020 ; 47( 12): 2159–2177.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/02664763.2019.1706725
Artigo de periodico
The Spike-and-Slab Lasso regression modeling with compositional covariates: an application on Brazilian children malnutrition data (2020)
Louzada, Francisco ; Shimizu, Taciana Kisaki Oliveira; Suzuki, Adriano Kamimura
Fonte: Statistical Methods in Medical Research. Unidades: ICMC, INTER: ICMC -UFSCAR
Assuntos: ANÁLISE DE SOBREVIVÊNCIA, ANÁLISE DE REGRESSÃO E DE CORRELAÇÃO
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ABNT
LOUZADA, Francisco e SHIMIZU, Taciana Kisaki Oliveira e SUZUKI, Adriano Kamimura. The Spike-and-Slab Lasso regression modeling with compositional covariates: an application on Brazilian children malnutrition data. Statistical Methods in Medical Research, v. 29, n. 5, p. 1434-1446, 2020Tradução . . Disponível em: https://doi.org/10.1177/0962280219863817. Acesso em: 11 nov. 2024.
APA
Louzada, F., Shimizu, T. K. O., & Suzuki, A. K. (2020). The Spike-and-Slab Lasso regression modeling with compositional covariates: an application on Brazilian children malnutrition data. Statistical Methods in Medical Research, 29( 5), 1434-1446. doi:10.1177/0962280219863817
NLM
Louzada F, Shimizu TKO, Suzuki AK. The Spike-and-Slab Lasso regression modeling with compositional covariates: an application on Brazilian children malnutrition data [Internet]. Statistical Methods in Medical Research. 2020 ; 29( 5): 1434-1446.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1177/0962280219863817
Vancouver
Louzada F, Shimizu TKO, Suzuki AK. The Spike-and-Slab Lasso regression modeling with compositional covariates: an application on Brazilian children malnutrition data [Internet]. Statistical Methods in Medical Research. 2020 ; 29( 5): 1434-1446.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1177/0962280219863817
Artigo de periodico
A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling (2019)
Saraiva, Erlandson Ferreira ; Pereira, C. A. B; Suzuki, Adriano Kamimura
Fonte: Journal of Statistical Computation and Simulation. Unidade: ICMC
Assuntos: CLUSTERS, ALGORITMOS ÚTEIS E ESPECÍFICOS, DISTRIBUIÇÕES (PROBABILIDADE)
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ABNT
SARAIVA, Erlandson Ferreira e PEREIRA, C. A. B e SUZUKI, Adriano Kamimura. A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling. Journal of Statistical Computation and Simulation, v. 89, n. 15, p. 2848-2870, 2019Tradução . . Disponível em: https://doi.org/10.1080/00949655.2019.1643345. Acesso em: 11 nov. 2024.
APA
Saraiva, E. F., Pereira, C. A. B., & Suzuki, A. K. (2019). A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling. Journal of Statistical Computation and Simulation, 89( 15), 2848-2870. doi:10.1080/00949655.2019.1643345
NLM
Saraiva EF, Pereira CAB, Suzuki AK. A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 15): 2848-2870.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/00949655.2019.1643345
Vancouver
Saraiva EF, Pereira CAB, Suzuki AK. A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 15): 2848-2870.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/00949655.2019.1643345
Artigo de periodico
A general long-term aging model with different underlying activation mechanism: modeling, bayesian estimation, and case influence diagnostics (2017)
Suzuki, Adriano Kamimura ; Barriga, Gladys D. C; Louzada, Francisco ; Cancho, Vicente Garibay
Fonte: Communications in Statistics - Theory and Methods. Unidade: ICMC
Assuntos: ANÁLISE DE SOBREVIVÊNCIA, DISTRIBUIÇÕES (PROBABILIDADE), MODELOS MATEMÁTICOS, INFERÊNCIA BAYESIANA, ESTATÍSTICA APLICADA, INFERÊNCIA ESTATÍSTICA
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ABNT
SUZUKI, Adriano Kamimura et al. A general long-term aging model with different underlying activation mechanism: modeling, bayesian estimation, and case influence diagnostics. Communications in Statistics - Theory and Methods, v. 46, n. 6, p. 3080-3098, 2017Tradução . . Disponível em: https://doi.org/10.1080/03610926.2015.1053945. Acesso em: 11 nov. 2024.
APA
Suzuki, A. K., Barriga, G. D. C., Louzada, F., & Cancho, V. G. (2017). A general long-term aging model with different underlying activation mechanism: modeling, bayesian estimation, and case influence diagnostics. Communications in Statistics - Theory and Methods, 46( 6), 3080-3098. doi:10.1080/03610926.2015.1053945
NLM
Suzuki AK, Barriga GDC, Louzada F, Cancho VG. A general long-term aging model with different underlying activation mechanism: modeling, bayesian estimation, and case influence diagnostics [Internet]. Communications in Statistics - Theory and Methods. 2017 ; 46( 6): 3080-3098.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/03610926.2015.1053945
Vancouver
Suzuki AK, Barriga GDC, Louzada F, Cancho VG. A general long-term aging model with different underlying activation mechanism: modeling, bayesian estimation, and case influence diagnostics [Internet]. Communications in Statistics - Theory and Methods. 2017 ; 46( 6): 3080-3098.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/03610926.2015.1053945
Artigo de periodico
Partitioning gene expression data by data-driven Markov chain Monte Carlo (2016)
Saraiva, E. F; Suzuki, Adriano Kamimura ; Louzada, Francisco ; Milan, Luis
Fonte: Journal of Applied Statistics. Unidade: ICMC
Assuntos: PROBABILIDADE, INFERÊNCIA BAYESIANA, INFERÊNCIA PARAMÉTRICA, INFERÊNCIA ESTATÍSTICA
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ABNT
SARAIVA, E. F et al. Partitioning gene expression data by data-driven Markov chain Monte Carlo. Journal of Applied Statistics, v. 43, n. 6, p. 1155-1173, 2016Tradução . . Disponível em: https://doi.org/10.1080/02664763.2015.1092113. Acesso em: 11 nov. 2024.
APA
Saraiva, E. F., Suzuki, A. K., Louzada, F., & Milan, L. (2016). Partitioning gene expression data by data-driven Markov chain Monte Carlo. Journal of Applied Statistics, 43( 6), 1155-1173. doi:10.1080/02664763.2015.1092113
NLM
Saraiva EF, Suzuki AK, Louzada F, Milan L. Partitioning gene expression data by data-driven Markov chain Monte Carlo [Internet]. Journal of Applied Statistics. 2016 ; 43( 6): 1155-1173.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/02664763.2015.1092113
Vancouver
Saraiva EF, Suzuki AK, Louzada F, Milan L. Partitioning gene expression data by data-driven Markov chain Monte Carlo [Internet]. Journal of Applied Statistics. 2016 ; 43( 6): 1155-1173.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/02664763.2015.1092113
Artigo de periodico
Predicting football scores via Poisson regression model: applications to the National Football League (2016)
Saraiva, E. F; Suzuki, Adriano Kamimura ; Filho, Ciro A. O; Louzada, Francisco
Fonte: Communications for Statistical Applications and Methods. Unidade: ICMC
Assuntos: PROBABILIDADE, INFERÊNCIA BAYESIANA, INFERÊNCIA PARAMÉTRICA, INFERÊNCIA ESTATÍSTICA
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ABNT
SARAIVA, E. F et al. Predicting football scores via Poisson regression model: applications to the National Football League. Communications for Statistical Applications and Methods, v. 23, n. 4, p. 297-319, 2016Tradução . . Disponível em: https://doi.org/10.5351/CSAM.2016.23.4.297. Acesso em: 11 nov. 2024.
APA
Saraiva, E. F., Suzuki, A. K., Filho, C. A. O., & Louzada, F. (2016). Predicting football scores via Poisson regression model: applications to the National Football League. Communications for Statistical Applications and Methods, 23( 4), 297-319. doi:10.5351/CSAM.2016.23.4.297
NLM
Saraiva EF, Suzuki AK, Filho CAO, Louzada F. Predicting football scores via Poisson regression model: applications to the National Football League [Internet]. Communications for Statistical Applications and Methods. 2016 ; 23( 4): 297-319.[citado 2024 nov. 11 ] Available from: https://doi.org/10.5351/CSAM.2016.23.4.297
Vancouver
Saraiva EF, Suzuki AK, Filho CAO, Louzada F. Predicting football scores via Poisson regression model: applications to the National Football League [Internet]. Communications for Statistical Applications and Methods. 2016 ; 23( 4): 297-319.[citado 2024 nov. 11 ] Available from: https://doi.org/10.5351/CSAM.2016.23.4.297
Artigo de periodico
The FGM long-term bivariate survival copula model: modeling, bayesian estimation, and case influence diagnostics (2013)
Louzada, Francisco ; Suzuki, Adriano Kamimura ; Cancho, Vicente Garibay
Fonte: Communications in Statistics - Theory and Methods. Unidade: ICMC
Assuntos: INFERÊNCIA BAYESIANA, ESTATÍSTICA APLICADA, INFERÊNCIA ESTATÍSTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LOUZADA, Francisco e SUZUKI, Adriano Kamimura e CANCHO, Vicente Garibay. The FGM long-term bivariate survival copula model: modeling, bayesian estimation, and case influence diagnostics. Communications in Statistics - Theory and Methods, v. 42, n. 4, p. 673-691, 2013Tradução . . Disponível em: https://doi.org/10.1080/03610926.2012.725147. Acesso em: 11 nov. 2024.
APA
Louzada, F., Suzuki, A. K., & Cancho, V. G. (2013). The FGM long-term bivariate survival copula model: modeling, bayesian estimation, and case influence diagnostics. Communications in Statistics - Theory and Methods, 42( 4), 673-691. doi:10.1080/03610926.2012.725147
NLM
Louzada F, Suzuki AK, Cancho VG. The FGM long-term bivariate survival copula model: modeling, bayesian estimation, and case influence diagnostics [Internet]. Communications in Statistics - Theory and Methods. 2013 ; 42( 4): 673-691.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/03610926.2012.725147
Vancouver
Louzada F, Suzuki AK, Cancho VG. The FGM long-term bivariate survival copula model: modeling, bayesian estimation, and case influence diagnostics [Internet]. Communications in Statistics - Theory and Methods. 2013 ; 42( 4): 673-691.[citado 2024 nov. 11 ] Available from: https://doi.org/10.1080/03610926.2012.725147

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