Publications

Most relevant papers

• Moreira, G. A. and Gamerman, D. (2022). Analysis of presence-only data via exact Bayes, with model and effects identification. Annals of Applied Statistics, 16.
• Gamerman, D., Ippoliti, L. and Valentini, P. (2022). A Dynamic Structural Equation Approach to Estimate the Short-Term Eects of Air Pollution on Human Health. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71, 739-769.
• Leonelli, M. and Gamerman, D. (2020). Semiparametric bivariate modelling with flexible extremal dependence. Statistics & Computing, 30, 221-236.
• Gonçalves, F. B. and Gamerman, D. (2018). Exact Bayesian inference in spatio-temporal Cox processes driven by multivariate Gaussian processes. Journal of the Royal Statistical Society: Series B, 80, 157-175.
• Ferreira, G. S. and Gamerman, D. (2015). Optimal Design in Geostatistics under Preferential Sampling (with discussion). Bayesian Analysis, 10, 711-758.
• Gamerman, D., Santos, T. R. and Franco, G. C. (2014). A non-Gaussian family of state-space models with exact marginal likelihood. Journal of Time Series Analysis, 35, 625-645.
• Nascimento, F. F., Gamerman, D. and Lopes, H. F. (2012). A semiparametric Bayesian approach to extreme value estimation. Statistics & Computing, 22, 661-675.
• Lopes, H. F., Salazar, E. and Gamerman, D. (2008). Spatial dynamic factor analysis. Bayesian Analysis, 3, 759-792.
• Gamerman, D. and Moreira, A. R. B. (2004). Multivariate spatial regression models. Journal of Multivariate Analysis, 91, 262-281.
• Gamerman, D. (1998). Markov chain Monte Carlo for dynamic generalized linear models. Biometrika, 85, 215-227.
• Gamerman, D. (1997). Sampling from the posterior distribution in generalized linear mixed models. Statistics & Computing, 7, 57-68.
• Gamerman, D. and Migon, H. S. (1993). Dynamic hierarchical models. Journal of the Royal Statistical Society, Series B, 55, 629-642.
• Gamerman, D. (1992). A dynamic approach to the statistical analysis of point processes. Biometrika, 79, 39-50.
• Gamerman, D. (1991). Dynamic Bayesian models for survival data. Applied Statistics, 40, 63-79.

Most recent papers

• Moreira, G. A. and Gamerman, D. (2022). Analysis of presence-only data via exact Bayes, with model and effects identification. Annals of Applied Statistics, 16.
• Gamerman, D., Ippoliti, L. and Valentini, P. (2022). A Dynamic Structural Equation Approach to Estimate the Short-Term Eects of Air Pollution on Human Health. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71, 739-769.
• Santos, T. R., Franco, G. C. and Gamerman, D. (2021). NGSSEML: Non-Gaussian state space with exact marginal likelihood. R Journal.
• Leonelli, M. and Gamerman, D. (2020). Semiparametric bivariate modelling with flexible extremal dependence. Statistics & Computing, 30, 221-236.
• Gamerman, D. (2019). Spatiotemporal point processes: regression, model specifications and future directions. Brazilian Journal of Probability and Statistics, 33, 686-705.
• Gonçalves, F. B. and Gamerman, D. (2018). Exact Bayesian inference in spatio-temporal Cox processes driven by multivariate Gaussian processes. Journal of the Royal Statistical Society: Series B, 80, 157-175.

Books

• Building a Platform for Data-Driven Pandemic Prediction: From Data Modelling to Visualisation - The CovidLP Project, Chapman and Hall: London, 2021, with Marcos Prates, Thais Paiva and Vinicius Mayrink.
• Statistical Inference: an Integrated Approach, Arnold: London, with Migon, H. S., 1999 (1st edition) and Chapman & Hall: London, 2014 (2nd edition), with Migon, H. S. and Louzada, F.
• Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Chapman & Hall: London, 1997 (1st. edition) and 2006 (2nd edition, with Hedibert F. Lopes)
• Modelos de espaço de estados: abordagens clássica and Bayesiana, ABE: São Paulo, with Glaura C. Franco and Thiago R. Santos, 2009.
• Modelagem de processos espaço-temporais, ABE: São Paulo, with Marina S. Paez, 2005.
• Simulação Estocástica via Cadeias de Markov, ABE: São Paulo, 1996.

• International Meeting of the Psychometric Society, July 2019
  Plenary talk:
  Dynamic generalized structural equation modeling, with application to the effect of pollution on health
• Workshop on Point Process Models, September 2018
  Plenary talk:
  Bayesian inference for spatiotemporal point processes (driven by multivariate Gaussian processes)
• VI Latin American Bayesian Congress (COBAL), June 2019
  Closing talk:
  Modeling exceedances in extreme value theory: foundations, regression, time series, and multivariate settings
• Modern Spatial Statistics Conference, in Honour of Julian Besag - May 2011
  Opening talk:
  Space-time modeling of coupled spatio-temporal environmental variables
• 8th. International Valencia Meeting on Bayesian Analysis - June 2006
  Plenary talk:
  Dynamic Gaussian process priors, with applications to the analysis of space-time data
• 7th. World Meeting of the International Society for Bayesian Analysis - May 2004
  Opening talk:
  Dynamic spatial models
• 17th. International Workshop on Statistical Modeling - July 2002
  Plenary talk:
  A latent approach to the statistical analysis of space-time data