Papers

  1. Gervini, D. (2022b). Spatial kriging for replicated temporal point processes. Spatial Statistics 51 100681.
  2. Gervini, D. (2022a). Doubly stochastic models for spatio-temporal covariation of replicated point processes. Canadian Journal of Statistics 50 287-303.
  3. Gervini, D. and Baur, T.J. (2020). Joint models for grid point and response processes in longitudinal and functional data. Statistica Sinica 30 1905-1924.
  4. Gervini, D. and Khanal, M. (2019). Exploring patterns of demand in bike sharing systems via replicated point process models.  Journal of the Royal Statistical Society Series C: Applied Statistics 68 585-602.
  5. Gervini, D. (2017). Multiplicative component models for replicated point processes. ArXiv 1705.09693.
  6. Gervini, D. (2016). Independent component models for replicated point processes. Spatial Statistics 18 474-488.
  7. Gervini, D. (2015). Dynamic retrospective regression for functional data. Technometrics 57 26-34.
  8. Gervini, D. (2015). Warped functional regression. Biometrika 102 1-14.
  9. Gervini, D. (2014). Analysis of Aneurisk65 data: warped logistic discrimination. Electronic Journal of Statistics 8 1930-1936.
  10. Gervini, D. and Carter, P.A. (2014). Warped functional analysis of variance. Biometrics 70 526-535.
  11. Gervini, D. (2012). Functional robust regression for longitudinal data. ArXiv 1211.7332.
  12. Gervini, D. (2012). Outlier detection and trimmed estimation for general functional data. Statistica Sinica 22 1639-1660.
  13. Gervini, D. (2010). The functional singular value decomposition for bivariate stochastic processes.  Computational Statistics and Data Analysis 54 163-172. (ArXiv 1211.7336).
  14. Hürtgen, H. and Gervini, D. (2009). Semiparametric shape-invariant models for periodic data. Journal of Applied Statistics 36 1055-1065.
  15. Gervini, D. (2009). Detecting and handling outlying trajectories in irregularly sampled functional datasets. The Annals of Applied Statistics 3 1758-1775.
  16. Gervini, D. (2008). Robust functional estimation using the median and spherical principal components. Biometrika 95 587-600.
  17. Rühlicke, R. and Gervini, D. (2008). Logistic discrimination with total variation regularization. Communications in Statistics – Simulation and Computation 37 1825-1838.
  18. Auer, P. and Gervini, D. (2008). Choosing principal components: a new graphical method based on Bayesian model selection.  Communications in Statistics – Simulation and Computation 37 962-977.
  19. Gervini, D. (2006). Free-knot spline smoothing for functional data. Journal of the Royal Statistical Society (Series B) 68 671-687.
  20. Gervini, D. (2005). Robust adaptive estimators for binary regression models. Journal of Statistical Planning and Inference 131 297-311.
  21. Gervini, D. and Gasser, T. (2005). Nonparametric maximum likelihood estimation of the structural mean of a sample of curves. Biometrika 92 801-820.
  22. Gervini, D. and Gasser, T. (2004). Self-modeling warping functions.  Journal of the Royal Statistical Society (Series B) 66 959-971.
    • An earlier version with more asymptotic stuff is available here.
  23. Gervini, D. and Rousson, V. (2004). Criteria for evaluating dimension-reducing components for multivariate data.  The American Statistician 58 72-76.
  24. Gasser, T., Gervini, D. and Molinari, L. (2004). Kernel estimation, shape-invariant modeling and structural analysis.  In Methods in Human Growth Research (eds. R. Hauspie, N. Cameron and L. Molinari), pp. 179-204. Cambridge University Press.
  25. Gervini, D. (2003). A robust and efficient adaptive reweighted estimator of multivariate location and scatter. Journal of Multivariate Analysis 84 116-144.
  26. Gervini, D. (2002). The influence function of the Stahel-Donoho estimator of multivariate location and scatter. Statistics & Probability Letters 60 425-435.
  27. Gervini, D. and Yohai, V.J. (2002). A class of robust and fully efficient regression estimators. Annals of Statistics 30 583-616.
  28. Gervini, D. and Yohai, V.J. (1998). Robust estimation of variance components. Canadian Journal of Statistics 26 419-430.

Last updated: 24 Oct 2023, 11:00 hs