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Contact details +64 (09) 414 0800 ext. 41330
Jose (Pepe) Romeo has a PhD in Statistics from University of Sao Paulo, Brazil. He has taught many courses related to Statistics at undergraduate and postgraduate level across different universities in Chile. Recently, he was a postdoctoral fellow in the Dept. of Statistics at the University of Auckland. He has interest in Applied Statistical Modelling. He also has interest in Handball, Surfing and Music.
Statistical Modelling | Biostatistics | Bayesian Inference | Regression models | Survival Analysis | Copula and Frailty models | Multivariate methods
1. 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.
2. 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.
3. Meyer, R. and Romeo, J.S. (2015). Bayesian semiparametric analysis of recurrent failure time data using copulas. Biometrical Journal, 57, 982-1001.
4. 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.
5. 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.
6. Roman, S.T., Romeo, J.S. and Salinas, V.H. (2014). Bayesian estimation of the limiting availability in the presence of right-censored data. METRON, 72, 247-267.
7. 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.
8. 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.
9. 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.
10. 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.
11. 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.
12. Salinas, V.H., Romeo, J.S. and Pe~na, J.A. (2010). On Bayesian estimation of a survival curve: comparative study and examples. Computational Statistics, 25, 375-389.
13. 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.
14. 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.
Field of research codes
Applied Statistics (010401): Biostatistics (010402): Mathematical Sciences (010000): Statistical Theory (010405): Statistics (010400)