Stefano Federico Tonellato

Personal Details

First Name:	Stefano
Middle Name:	Federico
Last Name:	Tonellato
Suffix:
RePEc Short-ID:	pto293

Affiliation

Dipartimento di Economia
Università Ca' Foscari Venezia

Venezia, Italy
http://www.unive.it/dip.economia
RePEc:edi:dsvenit (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

1. Stefano F. Tonellato, 2005. "Identifiability Conditions for Spatio-Temporal Bayesian Dynamic Linear Models," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 81-101.

   Cited by:

   1. Chiara Mazzetta & Steve Brooks & Stephen N. Freeman, 2007. "On Smoothing Trends in Population Index Modeling," Biometrics, The International Biometric Society, vol. 63(4), pages 1007-1014, December.

2. Stefano F. Tonellato, 2001. "A multivariate time series model for the analysis and prediction of carbon monoxide atmospheric concentrations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 187-200.

   Cited by:

   1. Giovanna Jona Lasinio & Francesco Lagona, 2002. "Selection of the neighborhood structure for space-time Markov random field models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(3), pages 293-311, October.
   2. Yi Liu & Gavin Shaddick & James V. Zidek, 2017. "Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 559-581, December.
   3. Nadja A. Leith & Richard E. Chandler, 2010. "A framework for interpreting climate model outputs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 279-296, March.

3. Patrick E. Brown & Gareth O. Roberts & Kjetil F. Kåresen & Stefano Tonellato, 2000. "Blur‐generated non‐separable space–time models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 847-860.

   Cited by:

   1. Christopher K. Wikle, 2003. "Hierarchical Models in Environmental Science," International Statistical Review, International Statistical Institute, vol. 71(2), pages 181-199, August.
   2. Christopher Wikle & Mevin Hooten, 2010. "A general science-based framework for dynamical spatio-temporal models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 417-451, November.
   3. Min Deng & Wentao Yang & Qiliang Liu & Yunfei Zhang, 2017. "A divide-and-conquer method for space–time series prediction," Journal of Geographical Systems, Springer, vol. 19(1), pages 1-19, January.
   4. Giacomini, Raffaella & Granger, Clive W.J., 2001. "Aggregationn of Space-Time Processes," University of California at San Diego, Economics Working Paper Series qt77f76455, Department of Economics, UC San Diego.
      • Raffaella Giacomini & Clive W.J. Granger, 2002. "Aggregation of Space-Time Processes," Boston College Working Papers in Economics 582, Boston College Department of Economics.
      • Giacomini, Raffaella & Granger, Clive W. J., 2004. "Aggregation of space-time processes," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 7-26.
   5. Ma, Chunsheng, 2003. "Nonstationary covariance functions that model space-time interactions," Statistics & Probability Letters, Elsevier, vol. 61(4), pages 411-419, February.
   6. Alexandre Rodrigues & Peter J. Diggle, 2010. "A Class of Convolution‐Based Models for Spatio‐Temporal Processes with Non‐Separable Covariance Structure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 553-567, December.
   7. Richardson, Robert & Kottas, Athanasios & Sansó, Bruno, 2017. "Flexible integro-difference equation modeling for spatio-temporal data," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 182-198.
   8. Serena Arima & Lorenza Cretarola & Giovanna Jona Lasinio & Alessio Pollice, 2012. "Bayesian univariate space-time hierarchical model for mapping pollutant concentrations in the municipal area of Taranto," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 75-91, March.
   9. Sigrist, Fabio & Künsch, Hans R. & Stahel, Werner A., 2015. "spate: An R Package for Spatio-Temporal Modeling with a Stochastic Advection-Diffusion Process," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i14).
   10. Tata Subba Rao & Sourav Das & Georgi N. Boshnakov, 2014. "A Frequency Domain Approach For The Estimation Of Parameters Of Spatio-Temporal Stationary Random Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(4), pages 357-377, July.
   11. Robert Richardson & Athanasios Kottas & Bruno Sansó, 2020. "Spatiotemporal modelling using integro‐difference equations with bivariate stable kernels," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1371-1392, December.
   12. Athanasios Kottas, 2018. "Discussion of paper “nonparametric Bayesian inference in applications” by Peter Müller, Fernando A. Quintana and Garritt L. Page," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 219-225, June.
   13. Giorgos Sermaidis & Omiros Papaspiliopoulos & Gareth O. Roberts & Alexandros Beskos & Paul Fearnhead, 2013. "Markov Chain Monte Carlo for Exact Inference for Diffusions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(2), pages 294-321, June.
   14. A. E. Madrid & J. M. Angulo & J. Mateu, 2016. "Point Pattern Analysis of Spatial Deformation and Blurring Effects on Exceedances," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 512-530, September.
   15. Kim, Yongku & Berliner, L. Mark, 2016. "Change of spatiotemporal scale in dynamic models," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 80-92.
   16. Harrison Quick & Sudipto Banerjee & Bradley P. Carlin, 2015. "Bayesian modeling and analysis for gradients in spatiotemporal processes," Biometrics, The International Biometric Society, vol. 71(3), pages 575-584, September.
   17. Ruiz-Medina, M.D. & Salmeron, R. & Angulo, J.M., 2007. "Kalman filtering from POP-based diagonalization of ARH(1)," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4994-5008, June.

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