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publications [2019/09/26 14:35]
admin
publications [2019/09/26 15:15]
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   - Damascena, A.S., Rocha, F.M.M. and Singer, J.M. (2019). Fitting linear mixed models under nonstandard assumptions:​ a Bayesian approach.\\ http://​www.ime.usp.br/​~jmsinger/​Textos/​Damascenaetal2019.pdf   - Damascena, A.S., Rocha, F.M.M. and Singer, J.M. (2019). Fitting linear mixed models under nonstandard assumptions:​ a Bayesian approach.\\ http://​www.ime.usp.br/​~jmsinger/​Textos/​Damascenaetal2019.pdf
   - Singer, J.M., Rocha, F.M.M., Pedroso-de-Lima,​ A.C., Silva< G.L., Coatti, G.C. and Zatz, M. (2019). Random changepoint segmented regression with smooth transition: an example with lateral amyotrophic sclerosis data.\\ http://​www.ime.usp.br/​~jmsinger/​Textos/​Singeretal2019b.pdf   - Singer, J.M., Rocha, F.M.M., Pedroso-de-Lima,​ A.C., Silva< G.L., Coatti, G.C. and Zatz, M. (2019). Random changepoint segmented regression with smooth transition: an example with lateral amyotrophic sclerosis data.\\ http://​www.ime.usp.br/​~jmsinger/​Textos/​Singeretal2019b.pdf
-  - Amorim, W.N., Pedroso-de-Lima,​ A.C., Singer, J.M., André, C.D.S., Andrade, M.F. and Artaxo, P. (2019). Tree-based models and machine learning techniques for accessing the effect of the gasoline/​ethanol share on ozone concentration ​+  - Amorim, W.N., Pedroso-de-Lima,​ A.C., Singer, J.M., André, C.D.S., Andrade, M.F. and Artaxo, P. (2019). Tree-based models and machine learning techniques for accessing the effect of the gasoline/​ethanol share on ozone concentration
 +  - Zitelli, P.M.Y., Gouvea M., Mazo, D.F., Singer, J.M.; Oliveira, C.P., Tanigawa, R.Y., Alves, V.A.F., Carrilho, F.J., Pessoa, M.G. (2019). Hepatitis E infection increases the risk of diabetes, hepatic decompensation,​ and mortality in HCV infected patients. 
 +  - Locosselli, G.M., Moreira, T.C., Chacón-Madrid,​ K., Arruda, M., Camargo, E.P., Kamigauti, L.Y., Trindade, R., Andrade, M.F., André, C.D.S., André, P.A., Singer, J.M., Saiki, M., Zacarelli-Marino,​ M.A., Saldiva, P.H.N. and Buckeridge, M. (2019). Spatial-temporal variability of metal pollution across an industrial district, evidencing the environmental inequality in São Paulo
  
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