Prado R. and West M. (2010) Time Series: Modeling, Computation, and Inference. CRC Press.



  • 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., Rodrguez 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 cation 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 E ects 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.


  • Cadonna, A., Kottas, A., Prado, R. (2016). Bayesian spectral for multiple spectral densities, University of California, Santa Cruz. Submitted.
  • Yu. C., Prado R., Rowe D. and Ombao H. (2016) A Bayesian Variable Selection Approach Yields Improved Brain Activation From Complex-Valued fMRI. Submitted.
  • Cadonna, A., Kottas, A., Prado, R. (2015). Bayesian mixture modeling for spectral density estimation. Submitted.