Papers (2006-2014)

  1. Daley, D., & Porcu, E. (2014). Dimension walks and Schoenberg spectral measures. Proceedings of the American Mathematical Society142(5), 1813-1824.
  2. Lagos-Álvarez, B., Ferreira, G., & Porcu, E. (2014). Modified Maximum Likelihood Estimation in Autoregressive Processes with Generalized Exponential Innovations. Open Journal of Statistics4(08), 620.
  3. Hristopulos, D. T., & Porcu, E. (2014). Multivariate Spartan spatial random field models. Probabilistic Engineering Mechanics37, 84-92.
  4. Shen, L., Ostoja-Starzewski, M., & Porcu, E. (2014). Bernoulli–Euler beams with random field properties under random field loads: fractal and Hurst effects. Archive of Applied Mechanics84(9-11), 1595-1626.
  5. Porcu, E., & Zastavnyi, V. (2014). Generalized Askey functions and their walks through dimensions. Expositiones Mathematicae32(2), 190-198.
  6. Gregori, P., Porcu, E., & Mateu, J. (2014). Models of covariance functions of Gaussian random fields escaping from isotropy, stationarity and non negativity. Image Analysis & Stereology33(1), 75-81.
  7. Guillot, G., Schilling, R. L., Porcu, E., & Bevilacqua, M. (2014). Validity of covariance models for the analysis of geographical variation. Methods in Ecology and Evolution5(4), 329-335.
  8. Crudu, F., Porcu, E. and Bevilacqua, M. (2013). A Note on the paper by Frick, Munk and Sieling.  Journal of the Royal Statistical Society, B,76(2). 
  9. López, F. and Porcu, E. (2014). Comment on Marron, J. S., & Alonso, A. M. (2014). Overview of object oriented data analysis. Biometrical Journal56(5), 732-753.
  10. Crudu, F., Porcu, E. and Bevilacqua, M. (2013). A Note on the paper by Frick, Munk and Sieling.  Journal of the Royal Statistical Society, B,76(2). 
  11. Cuevas, F., Porcu, E., & Vallejos, R. (2013). Study of spatial relationships between two sets of variables: a nonparametric approach. Journal of Nonparametric Statistics25(3), 695-714.
  12. Vallejos, R., Porcu, E., Bevilacqua, M. (2013). Discussion of the paper “How to find an appropriate clustering for mixed type variables with application to socio-economic variables” by Hennig C. and Liao, T. F. Journal of The Royal Statistical Society C62(3), 309–369.
  13. Porcu, E., & Schilling, R. L. (2013). Addendum to “From Schoenberg to Pick–Nevanlinna: Towards a complete picture of the variogram class”. Bernoulli19(5B), 2768-2768.
  14. Amo-Salas, M., López-Fidalgo, J., & Porcu, E. (2013). Optimal designs for some stochastic processes whose covariance is a function of the mean. Test22(1), 159-181.
  15. Porcu, E., Daley, D. J., Buhmann, M., & Bevilacqua, M. (2013). Radial basis functions with compact support for multivariate geostatistics. Stochastic environmental research and risk assessment27(4), 909-922.
  16. Bevilacqua, M., Gaetan, C., Mateu, J., & Porcu, E. (2012). Estimating space and space-time covariance functions for large data sets: a weighted composite likelihood approach. Journal of the American Statistical Association107(497), 268-280.
  17. Leiva, V. and Porcu, E. (2012). Discussion to the paper ”Experimental designs for identifying causal mechanisms” by K. Imai, D. Tingley and T. Yamamoto. J. R. Statist. Soc. A, pp. 1-27.
  18. Zini, A. and Porcu, E. (2012). Discussion to the paper: Probabilistic index models, by Thas, O., De Neve, J., Clement, L. & and Ottoy, J-P. Journal of the Royal Statistical Society, B, 74.
  19. Porcu, E., Giraldo, R. and Alonso, C. (2012). Discussion to the paper: Vignettes and health systems responsiveness in cross-country comparative analyses. Journal of the Royal Statistical Society, A. 175, 337-369.
  20. Porcu, E., Mateu, J., Gregori, P., & Ostoja-Starzewski, M. (2012). New classes of spectral densities for lattice processes and random fields built from simple univariate margins. Stochastic environmental research and risk assessment, 26(4), 479-490.
  21. Gonzalez–Manteiga, W. and Porcu, E. (2012). Discussion to the paper: ”Optimum design of exper- iments for statistical inference” by Gilmour and Trinca. Journal of the Royal Statistical Society, A. 61.
  22. Hristopoulos, D. and Porcu, E. (2012). Discussion to the Paper: Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the Akaike information criterion – Bayesian information criterion dilemma by van Erven, Gru ̈nwald, de Rooij. Journal of the Royal Statistical Society, B. 74.
  23. Porcu, E., & Zastavnyi, V. (2011). Characterization theorems for some classes of covariance functions associated to vector valued random fields. Journal of Multivariate Analysis102(9), 1293-1301.
  24. Fernandez-Aviles, G., Montero, J.M., Porcu, E. and Schlather, M. (2012). Introduction to Space–Time processes for Environmental Sciences. To appear to Advances and Challenges in Space-Time Modelling of Natural Events, E. Porcu, J.M. Montero and M. Schlather (Eds). Springer Verlag, Vol. 207, 1–24.
  25. Porcu, E. and Stein, M.L. (2012). On some local, global and regularity behaviour of some classes of covariance functions. Advances and Challenges in Space-Time Modelling of Natural Events, E. Porcu, J.M. Montero and M. Schlather (Eds). Springer Verlag, Vol. 207, 221–239.
  26. Porcu, E., Alonso, C. and Zini, A. (2012). Discussion to the paper: Statistical methods for healthcare regulation: rating, screening and surveillance, by Spiegelhalter, D., Sherlaw-Johnson, C., Bardsley, M., Blunt, I., Wood, C. & Grigg, O. Journal of the Royal Statistical Society, A, 175.
  27. Porcu, E., Giraldo, R. and Alonso, C. (2012). Discussion to the paper: Vignettes and health systems responsiveness in cross-country comparative analyses. Journal of the Royal Statistical Society, A. 175.
  28. Papaspiliopoulos, O. and Porcu, E. (2011). Comment on the paper by Lindgren, Rue and Lindstrom. Journal of the Royal Statistical Society, B. 73(4), 423-498.
  29. Ober, U., Erbe, M., Long, N., Porcu, E., Schlather, M., & Simianer, H. (2011). Predicting genetic values: a kernel-based best linear unbiased prediction with genomic data. Genetics188(3), 695-708.
  30. Ruiz-Medina, M. D., Porcu, E., & Fernandez-Pascual, R. (2011). The Dagum and auxiliary covariance families: Towards reconciling two-parameter models that separate fractal dimension and the Hurst effect. Probabilistic Engineering Mechanics26(2), 259-268.
  31. Porcu, E., & Schilling, R. L. (2011). From Schoenberg to Pick–Nevanlinna: Toward a complete picture of the variogram class. Bernoulli17(1), 441-455.
  32. Mateu, J., Montes, F., & Porcu, E. (2010). Spatio‐temporal stochastic modelling: environmental and health processes. Environmetrics: The official journal of the International Environmetrics Society21(3‐4), 221-223.
  33. Mateu, J., Lorenzo, G., & Porcu, E. (2010). Features detection in spatial point processes via multivariate techniques. Environmetrics: The official journal of the International Environmetrics Society21(3‐4), 400-414.
  34. Martínez-Ruiz, F., Mateu, J., Montes, F., & Porcu, E. (2010). Mortality risk assessment through stationary space–time covariance functions. Stochastic Environmental Research and Risk Assessment24(4), 519-526.
  35. Porcu, E., Mateu, J., & Comas, C. (2010). A note on continuous spatial-temporal dynamics of stochastic processes. Communications in Statistics—Theory and Methods39(19), 3472-3484.
  36. Porcu, E., Matkowski, J., & Mateu, J. (2010). On the non-reducibility of non-stationary correlation functions to stationary ones under a class of mean-operator transformations. Stochastic environmental research and risk assessment, 24(5), 599-610.
  37. Bevilacqua, M., Mateu, J., Porcu, E., Zhang, H., & Zini, A. (2010). Weighted composite likelihood-based tests for space-time separability of covariance functions. Statistics and Computing20(3), 283-293.
  38. Porcu, E., Mateu, J., & Christakos, G. (2009). Quasi-arithmetic means of covariance functions with potential applications to space–time data. Journal of Multivariate Analysis100(8), 1830-1844.
  39. Porcu, E., Crujeiras, R., Mateu, J., & Gonzalez-Manteiga, W. (2009). On the second order properties of the multidimensional periodogram for regularly spaced data. Theory of Probability & Its Applications53(2), 349-356.
  40. Porcu, E., Gregori, P., & Mateu, J. (2009). Archimedean spectral densities for nonstationary space-time Geostatistics. Statistica Sinica, 273-286.
  41. Berg, C., Mateu, J., & Porcu, E. (2008). The Dagum family of isotropic correlation functions. Bernoulli14(4), 1134-1149.
  42. Debón, A., Montes, F., Mateu, J., Porcu, E., & Bevilacqua, M. (2008). Modelling residuals dependence in dynamic life tables: A geostatistical approach. Computational Statistics & Data Analysis52(6), 3128-3147.
  43. Porcu, E., Mateu, J., & Saura, F. (2008). New classes of covariance and spectral density functions for spatio-temporal modelling. Stochastic Environmental Research and Risk Assessment22(1), 65-79.
  44. Mateu, J., Porcu, E., & Gregori, P. (2008). Recent advances to model anisotropic space–time data. Statistical Methods and Applications17(2), 209-223.
  45. Gregori, P., Porcu, E., Mateu, J., & Sasvári, Z. (2008). On potentially negative space time covariances obtained as sum of products of marginal ones. Annals of the Institute of Statistical Mathematics60(4), 865-882.
  46. Mateu, J., Lorenzo, G., & Porcu, E. (2007). Detecting features in spatial point processes with clutter via local indicators of spatial association. Journal of Computational and Graphical Statistics16(4), 968-990.
  47. Mateu, J., Porcu, E., Christakos, G., & Bevilacqua, M. (2007). Fitting negative spatial covariances to geothermal field temperatures in Nea Kessani (Greece). Environmetrics: The official journal of the International Environmetrics Society18(7), 759-773.
  48. Porcu, E., Gregori, P., & Mateu, J. (2007). La descente et la montée étendues: the spatially d-anisotropic and the spatio-temporal case. Stochastic Environmental Research and Risk Assessment21(6), 683-693.
  49. Mateu, J., Porcu, E., & Nicolis, O. (2007). A note on decoupling of local and global behaviours for the Dagum random field. Probabilistic Engineering Mechanics22(4), 320-329.
  50. Mateu, J., Juan, P., & Porcu, E. (2007). Geostatistical analysis through spectral techniques: some words of caution. Communications in Statistics—Simulation and Computation36(5), 1035-1051.
  51. Porcu, E., Mateu, J., & Bevilacqua, M. (2007). Covariance functions that are stationary or nonstationary in space and stationary in time. Statistica Neerlandica61(3), 358-382.
  52. Porcu, E., Mateu, J., Zini, A., & Pini, R. (2007). Modelling spatio-temporal data: A new variogram and covariance structure proposal. Statistics & probability letters77(1), 83-89.
  53. Yu, K., Mateu, J., & Porcu, E. (2007). A kernel‐based method for nonparametric estimation of variograms. Statistica Neerlandica61(2), 173-197.
  54. MATEU, J., COMAS, C., PORCU, E. & LOPEZ, J.A. (2006). Probability mapping in cluster spatial processes. Computational and analytical solutions through copulas. Lecture Series on Computer and Computational Sciences (ICCMSE 2006). Vol. 7, T. Simos & G. Maroulis (Eds.), pp. 376-379. ISBN: 90-04-15542-2.
  55. Porcu, E., Gregori, P., & Mateu, J. (2006). Nonseparable stationary anisotropic space–time covariance functions. Stochastic Environmental Research and Risk Assessment21(2), 113-122.
  56. Fassò, A., Esposito, A., Porcu, E., Reverberi, A. P., & Vegliò, F. (2003). Statistical sensitivity analysis of packed column reactors for contaminated wastewater. Environmetrics: The official journal of the International Environmetrics Society14(8), 743-759.