Artículos:

 

 

 

36. Alonso, A. M., Galeano, P. and Peña, D. (2020) A robust procedure to build Dynamic Factor Models with Cluster Structure. Journal of Econometrics, accepted for publication.

 

35. Virbickaite, A., Lopes, H. F., Ausín, M. C. and Galeano, P. (2019) Particle learning for Bayesian semi-parametric stochastic volatility model. Econometric Reviews, 38, 1007-1023.

 

34. Galeano, P. and Peña, D. (2019) Rejoinder on: Data science, big data and statistics. Test, 28, 363-368.

 

33. Galeano, P. and Peña, D. (2019) Data science, big data and statistics. Test, 28, 289-329. (Invited paper with discussion).

 

32. Febrero-Bande, M., Galeano, P. and González-Manteiga, W. (2019) Estimation, imputation and prediction for the functional linear model with scalar response with responses missing at random. Computational Statistics and Data Analysis, 131, 91-103.

 

31. Nguyen, H., Ausín, M. C. and Galeano, P. (2019) Parallel Bayesian inference for high-dimensional dynamic factor copulas. Journal of Financial Econometrics, 17, 118-151.

 

30. Galeano, P. and Wied, D. (2017) Dating multiple change points in the correlation matrix. Test, 26, 331-352.

 

29. Febrero-Bande, M., Galeano, P. and González-Manteiga, W. (2017) Parameter estimation of the functional linear model with scalar response with responses missing at random. In: Functional Statistics and Related Fields, 26, 105-111.

 

28. Febrero-Bande, M., Galeano, P. and González-Manteiga, W. (2017) Functional principal component regression and functional partial least squares regression: an overview and a comparative study. International Statistical Review, 85, 61-83.

 

27. Pape, K., Wied, D. and Galeano, P. (2016) Monitoring multivariate variance changes. Journal of Empirical Finance, 39, 54-68.

 

26. Sguera, C., Galeano, P. and Lillo, R. E. (2016) Functional outlier detection by a local depth with application to NOx levels. Stochastic Environmental Research and Risk Assessment, 30, 1115-1130.

 

25. Virbickaite, A., Ausín, M. C. and Galeano, P. (2016) A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection. Computational Statistics and Data Analysis, 100, 814-829.

 

24. Galeano, P., Joseph, E. and Lillo, R. E. (2015) The Mahalanobis distance for functional data with applications to classification. Technometrics, 57, 281-291.

 

23. Virbickaite, A., Ausín, M. C. and Galeano, P. (2015) Bayesian inference methods for univariate and multivariate GARCH models: a survey. Journal of Economic Surveys, 29, 76-96.

 

22. Sguera, C., Galeano, P. and Lillo, R. E. (2014) Spatial Depth-based classification for functional data. TEST, 23, 725-750.

 

21. Galeano, P. and Wied, D. (2014) Multiple break detection in the correlation structure of random variables. Computational Statistics and Data Analysis, 76, 262-282.

 

20. Ausín, M. C., Galeano, P. and Ghosh, P. (2014) A semiparametric Bayesian approach to the analysis of financial time series with applications to Value at Risk estimation. European Journal of Operational Research, 232, 350-358.

 

19. Galeano, P. and Peña, D. (2013) Finding outliers in linear and nonlinear time series. In: Robustness and Complex Data Structures. Springer, Heidelberg.

 

18. Wied, D. and Galeano, P. (2013) Monitoring correlation change in a sequence of random variables. Journal of Statistical Planning and Inference, 143, 186-196.

 

17. Galeano, P. (2012) Comments on Some recent theory for autoregressive count time series by Dag Tjostheim. TEST, 21, 455-458.

 

            16. Galeano, P. and Peña, D. (2012) Additive outlier detection in seasonal ARIMA model by a modified Bayesian Information Criterion. In Economic Time Series, Modeling and Seasonality, Chapman & Hall, New York.

 

            15. Galeano, P. and Tsay, R. S. (2010) Shifts in individual parameters of a GARCH model. Journal of Financial Econometrics, 8, 122-153.

 

            14. Galeano, P. and Ausín, M. C. (2010) The Gaussian mixture dynamic conditional correlation model: Parameter estimation, Value at Risk calculation and portfolio selection. Journal of Business and Economic Statistics, 28, 559-571.

 

            13. Febrero, M., Galeano, P. and González-Manteiga, W. (2010) Measures of influence for the functional linear model with scalar response. Journal of Multivariate Analysis, 101, 327-339.

 

            12. Febrero, M., Galeano, P. and González-Manteiga, W. (2008) Outlier detection in functional data by depth measures with application to identify abnormal NOx levels. Environmetrics, 19, 331-345.

 

            11. Febrero, M., Galeano, P. and González-Manteiga, W. (2008) Influence in the functional linear model with scalar response. In Functional and Operational Statistics, Springer, New York.

 

            10. Peña, D. and Galeano, P. (2008) An unified approach to model selection, discrimination, goodness of fit and outliers in time series. In Advances in Mathematical and Statistical Modeling, Birkhäuser, Boston.

 

            9. Febrero, M., Galeano, P. and González-Manteiga, W. (2007) A functional analysis of NOx levels: location and scale estimation and outlier detection. Computational Statistics, 22, 411-427.

 

            8. Galeano, P. (2007) The use of cumulative sums for detection of changepoints in the rate parameter of a Poisson process. Computational Statistics and Data Analysis, 51, 6151-6165.

 

            7. Galeano, P. and Peña, D. (2007) Improved model selection criteria for SETAR time series models. Journal of Statistical Planning and Inference, 137, 2802-2814.

 

            6. Galeano, P. and Peña, D. (2007) On the connection between model selection criteria and quadratic discrimination in ARMA time series models. Statistics and Probability Letters, 77, 896-900.

 

            5. Ausín, M. C. and Galeano, P. (2007) Bayesian estimation of the Gaussian mixture GARCH model. Computational Statistics and Data Analysis, 51, 2636-2652.

 

            4. Galeano, P. and Peña, D. (2007) Covariance changes detection in multivariate time series. Journal of Statistical Planning and Inference, 137, 194-211.

 

            3. Galeano, P., Peña, D. and Tsay, R. (2006) Outlier detection in multivariate time series by projection pursuit. Journal of the American Statistical Association, 101, 654-669.

 

            2. Galeano, P. and Peña, D. (2005) A note on prediction and interpolation errors in time series. Statistics and Probability Letters, 73, 71-78.

 

            1. Galeano, P. and Peña, D. (2000) Multivariate analysis in vector time series. Resenhas, the Journal of the Institute of Mathematics and Statistics of the University of Sao Paolo, 4, 383-403.

 

Libros:

 

1. Martín Municio, A., Espasa, A., Girón, J., Peña, D., Benito, M., Cañada, A., Galeano, P. y Garcia, C. E. (2003) El valor económico de la lengua española. Espasa-Calpe, Madrid.