Jose S. Romeo (Pepe)


**Currently in New Zealand on sabbatical leave**


• Phone: +56 227182060
• Office: 413 DMCC
• e-mail: Esta dirección de correo electrónico está protegida contra spambots. Usted necesita tener Javascript activado para poder verla.



Academic Position: Assistant Professor

Academic Degrees:

• Statistical Engineer, Departamento de Matematica, Universidad de Santiago, Chile, 2000
• PhD in Statistics, Instituto de Matematica e Estatistica, Universidade de Sao Paulo, Brazil, 2005
• Postdoctorate in Statistics, Department of Statistics, The University of Auckland, New Zealand, 2015

Handball player and beginner surfer ;-)

Pepe's main research interests:

• Statistical Modelling
• Biostatistics
• Bayesian Inference
• Regression Models
• Survival Analysis
• Copula and Frailty Models


• Gallardo, D.I., Romeo, J.S. and Meyer, R. (2016). A simplified estimation procedure based on the EM algorithm for the power series cure rate model. Communications in Statistics - Simulation and Computation, doi:10.1080/03610918.2016.1202276.

• Poshdar, M., Gonzalez, V.A., Raftery, G.M., Orozco, F., Romeo, J.S. and Forcael, E. (2016). A probabilistic-based method to determine optimum size of project buffer in construction schedules. Journal of Construction Engineering and Management, doi:10.1061/(ASCE)CO.1943-7862.0001158.

• Romeo, J.S. and Meyer, R. (2015). Bayesian approach for modelling bivariate survival data through the PVF copula. In Friedl, H. and Wagner, H. (Eds.), Proceedings of the 30th International Workshop on Statistical Modelling, vol. 2. Linz, Austria, 239-242.

• Meyer, R. and Romeo, J.S. (2015). Bayesian semiparametric analysis of recurrent failure time data using copulas. Biometrical Journal, 57, 982-1001.

• Reyes-Lopez, F.E., Romeo, J.S., Vallejos-Vidal, E., Reyes-Cerpa, S., Sandino, A.M., Tort, L., Mackenzie, S. and Imarai, M. (2015). Differential immune gene expression profiles in susceptible and resistant full-sibling families of Atlantic salmon (Salmo salar) challenged with infectious pancreatic necrosis virus (IPNV). Developmental & Comparative Immunology, 53, 210-221.

• Romeo, J.S., Meyer, R. and Reyes-Lopez, F. (2014). Hierarchical failure time regression using mixtures for classification of the immune response of Atlantic salmon. Journal of Agricultural, Biological, and Environmental Statistics, 19(4), 501-521.

• Roman, S.T., Romeo, J.S. and Salinas-Torres, V.H. (2014). Bayesian estimation of the limiting availability in the presence of right-censored data. METRON, 72(3), 247-267.

• Bazan, J.L., Romeo, J.S. and Rodrigues, J. (2014). Bayesian skew-probit regression for binary response data. Brazilian Journal of Probability and Statistics, 28, 467-482.

• Torres-Aviles, F., Romeo, J.S. and Lopez-Kleine, L. (2014). Data mining and influential analysis of gene expression data for plant resistance genes identification in tomato (Solanum lycopersicum). Electronic Journal of Biotechnology, 17, 79-82.

• Lopez-Kleine, L., Romeo, J.S. and Torres-Aviles, F. (2013). Gene functional prediction using clustering methods for the analysis of tomato microarray data. In Mohamad, M.S., Nanni, L., Rocha, M.P. and Fdez-Riverola, F. (Eds.), 7th International Conference on Practical Applications of Computational Biology & Bioinformatics, Advances in Intelligent Systems and Computing, vol. 222, Springer International Publishing, Switzerland, 1-6.

• Romeo, J.S., Torres-Aviles, F. and Lopez-Kleine, L. (2013). Detection of influent virulence and resistance genes in microarray data through quasi likelihood modeling. Molecular Genetics and Genomics, 288, 49-61.

• Romeo, J.S., Tanaka, N.I., Pedroso-de-Lima, A.C. and Salinas-Torres, V.H. (2013). Large sample properties for a class of copulas in bivariate survival analysis. Metrika, 76, 997-1015.

• Salinas, V.H., Romeo, J.S. and Peña, J.A. (2010). On Bayesian estimation of a survival curve: comparative study and examples.Computational Statistics, 25, 375-389.

• Diaz-Ledezma, C., Urrutia, J., Romeo, J.S., Chelen, A., Gonzalez-Wilhelm, L. and Lavarello, C. (2009). Factors associated with variability in length of sick leave because of acute low back pain in Chile. The Spine Journal, 9, 1010-1015.

• Romeo, J.S., Tanaka, N.I. and Pedroso de Lima, A.C. (2006). Bivariate survival modeling: a Bayesian approach based on copulas. Lifetime Data Analysis, 12, 205-222.


• Romeo, J.S., Meyer, R. and Gallardo, D.I. Bayesian multivariate survival analysis using the power variance function copula. Submitted.

• Valdebenito, A., Arellano-Valle, R.B., Romeo, J.S. and Torres-Aviles, F.J. A skew-normal dynamic linear model and Bayesian forecasting. Submitted.