Department of Statistics


Jose Romeo

Jose Romeo

Job title: Postdoctoral Research Fellow
Phone: +64 9 373 7599 ext 88364
Office: 303.222 Science Centre
Email: j.romeo@auckland.ac.nz

Biography

### # Now I'm in the School of Environment, Room 736, Level 7, Building 201N, 10 Symonds Street. Phone ext 88202 # ###

• Statistical Engineer from University of Santiago, Chile

• PhD in Statistics from University of Sao Paulo, Brazil

• Handball player and intermediate surfer ;-)

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Further Information

Pepe's research interests include:

Survival analysis | Copula and Frailty models | Bayesian inference | Biostatistics | Regression models

>> Publications:

• 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.

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• Meyer, R. and Romeo, J.S. Bayesian semi-parametric analysis of recurrent failure time data using copulas. Submitted.

• Reyes-Lopez, F.E., Romeo, J.S., Vallejos-Vidal, E., Reyes-Cerpa, S., Sandino, A.M., Vidal, R., Tort, L., Mackenzie, S. and Imarai, M. Searching for IPN-specific gene expression profiles in susceptible and resistant full-sibling families of Atlantic salmon (Salmo salar). Submitted.

• Poshdar, M., Gonzalez, V.A., Raftery, G.M., Orozco, F., Romeo, J.S. and Forcael, E. A probabilistic-based method to determine optimum size of project buffer in construction schedules. Submitted.

• Romeo, J.S., Meyer, R. and Gallardo, D.I. The power variance function copula model in bivariate survival analysis. Submitted.

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