BOOKS
Prado R., Ferreira M. and West M. (2021) Time Series: Modeling, Computation, and Inference. (Second Edition, Forthcoming July 2021) CRC Press.

SELECTED PAPERS

• Barata, R., Prado R. and Sanso B. (2021) Fast inference for time-varying quantiles via flexible dynamic models with application to the characterization of atmospheric rivers. Annals of Applied Statistics (accepted).
• Spencer, D., Guhaniyogi, R. and Prado, R. (2020). Bayesian mixed effect sparse tensor response regression model with joint estimation of activation and connectivity. Psychometrika, to appear.
• Zhao W. and Prado R. (2020) Efficient Bayesian PARCOR approaches for dynamic modeling of multivariate time series. Journal of Time Series Analysis.
• Barata R., Prado R., Sanso B. (2019) Comparison and assessment of large-scale surface temperature in climate model simulations. Advances in Statistical Climatology, Meteorology and Oceanography.
• Cadonna, A., Kottas, A., Prado, R. (2019). Bayesian mixture modeling for multiple spectral densities. Journal of the American Statistical Association.
• Yu. C., Prado R., Rowe D. and Ombao H. (2018) A Bayesian Variable Selection Approach Yields Improved Brain Activation From Complex-Valued fMRI. Journal of the American Statistical Association.
• Cadonna A., Kottas A., and Prado R. (2017) Bayesian mixture modeling for spectral density estimation. Statistics and Probability Letters 125, 189-195.
• Yu Z., Ombao H., Prado R., Quinlan E.B., and Cramer S. (2016) Understanding the impact of stroke on brain motor function: A hierarchical Bayesian approach. Journal of the American Statistical Association,  111: 549-563.
• Macaro C. and Prado R. (2014) Spectral decompositions of multiple time series: A Bayesian nonparametric approach. Psychometrika, 79(1): 105-129.
• Prado R. and Lopes H. (2013) Sequential parameter learning and filtering in structured autoregressive state-space models. Statistics and Computing, Vol. 23(1), pp. 43-57.
• Prado R. (2013) Sequential estimation of mixtures of structured autoregressive models. Computational Statistics and Data Analysis, Vol. 58, pp. 58-70.
• Datta S., Rodriguez A., and Prado R. (2012) Bayesian semiparametric regression models to characterize molecular evolution. BMC Bioinformatics, Vol. 13: 278.
• Datta S., Prado R., Rodriguez A., and Escalante A.A. (2010) Characterizing molecular adaptation: A hierarchical approach to assess the selective influence of amino acid properties. Bioinformatics, 26(22):2818-25.
• Merl D., Prado R. and Escalante A.A. (2008) Assessing the Effect of Selection at the Amino Acid Level in Malaria Antigen Sequences via Bayesian Generalized Linear Models. Journal of the American Statistical Association, Vol. 103, number 484, pp. 1496-1507.
• Prado R., Molina F.J. and Huerta G. (2006) Multivariate Time Series Modeling and Classification Via Hierarchical VAR Mixtures. Computational Statistics and Data Analysis, Vol. 51 (3), pp. 1445-1442.
• Huerta G. and Prado, R. (2006) Structured Priors for Multivariate Time Series. Journal of Statistical Planning and Inference, Vol. 136, pp. 3802-3821.
• Prado R. and Huerta G. (2002) Time-Varying Autoregressions with Model Order Uncer- tainty. Journal of Time Series Analysis, Vol. 23, pp. 599-618. Blackwell Publishers Ltd, Oxford, UK and Boston, USA.
• Prado R., West M. and Krystal A. (2001) Multi-Channel EEG Analyses Via Dynamic Regression Models with Time-Varying Lag/Lead Structure. Journal of the Royal Statistical Society, Series C (Applied Statistics), Vol. 50, pp. 95-109.
• Krystal A.D., West M., Prado R., Greenside H., Zoldi S., Weiner R. (2000) EEG Eects of ECT: Implications for rTMS. Depression and Anxiety, Vol. 12, pp. 157-165.
• West M., Prado R. and Krystal A.D. (1999) Evaluation and Comparison of EEG Traces: Latent Structure in Non-Stationary Time Series. Journal of the American Statistical Association, Vol. 94, pp. 1083-1095.
• Krystal A.D., Prado R. and West M. (1999) New Methods of Time Series Analysis of Non-Stationary EEG Data: Eigenstructure Decompositions of Time-varying Autoregressions. Clinical Neurophysiology, Vol. 110, pp. 2197-2206.