Bruno Santos

My current research interests include extensions of quantile regression models for data presented in the interval (0,1), and with a zero inflated situation for this type of data as well. I also have interest for model selection methods regarding the class of quantile regression models.

Master's dissertation (in portuguese)

Publications:

  1. Santos, B. and Elian, S. (2012). Analysis of residuals in quantile regression: an application to income data in Brazil. Proceedings of the 27th International Workshop on Statistical Modelling (Arnost Komarek, Stanislav Nagy, editors), vol. 2, 723-728.
  2. Santos, B. and Bolfarine, H. (2013). A two-part model using quantile regression under a Bayesian perspective. Proceedings of the 28th International Workshop on Statistical Modelling (Vito M.R. Muggeo, Vincenza Capursi, Giovanni Boscaino, Gianfranco Lovison, editors), vol. 1, 200-205.
  3. Alencar, A. and Santos, B. (2014). Association of pollution with quantiles and expectations of the hospitalization rate of elderly people by respiratory diseases in the city of São Paulo, Brazil. Environmetrics. DOI: 10.1002/env.2274. link.
  4. Santos, B. and Bolfarine, H. (2014). Bayesian analysis for zero-or-one inflated proportion data using quantile regression. Journal of Statistical Computation and Simulation. DOI: 10.1080/00949655.2014.986733. link
  5. Santos, B. and Elian, S. (2015). Influence measures in quantile regression models. Communications in Statistics - Theory and Methods. DOI: 10.1080/03610926.2013.799699. link
  6. Santos, B. and Bolfarine, H. (2015). Analysis of Brazil’s presidential election via Bayesian spatial quantile regression. Proceedings of the 30th International Workshop on Statistical Modelling (Herwig Friedl, Helga Wagner, editors), vol. 2, 239-242.
  7. Santos, B. and Bolfarine, H. (2016). Bayesian quantile regression analysis for continuous data with a discrete component at zero. link
  8. Santos, B. and Bolfarine, H. (2016). On Bayesian quantile regression and outliers. link

Working papers:

  1. Santos, B. and Bolfarine, H. (2015). Analysis of Brazil’s presidential election via Bayesian spatial quantile regression. In preparation.

Short courses given: