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SONIA PETRONE

SONIA PETRONE
Docente Ordinario
Dipartimento di Scienze delle Decisioni
sonia.petrone@unibocconi.it
Orario di ricevimento
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Insegnamenti a.a. 2013/2014

20236 TIME SERIES ANALYSIS OF ECONOMIC-FINANCIAL DATA
40008 TIME SERIES ANALYSIS
40042 MIXTURE MODELS
40202 TIME SERIES AND BAYESIAN ECONOMETRICS
Insegnamenti a.a. precedenti


Note biografiche

Laurea con lode in Discipline Economiche e Sociali presso l'Università Bocconi. Dottorato di ricerca in Statistica metodologica, Università di Trento (1989).


Curriculum Accademico

Sonia Petrone e' Bocconi Full Professor in Statistica. Ha insegnato precedentemente presso l'Università di Pavia e l'Università dell'Insubria. E' Direttrice del PhD in Statistics dell'Universita' Bocconi. Ha fatto parte del Comitato Ricerca (CORI) Bocconi nel 2012. Ha svolto periodi di ricerca presso diverse università e istituti di ricerca esteri, fra cui Stanford University, University of Cornell, University of Washington, Duke University, Institute for Mathematics and its Applications - University of Minnesota, Indian Statistical Institute, Russian Academy of Science, Universidad Catòlica de Chile.

E'  President 2014 della International Society for Bayesian Analysis (ISBA) . Ha servito nel Board of Directors dell'ISBA 2002-2004 e 2008-2010. E' membro eletto del Council dell''Institute of Mathematical Statistics (IMS), per il 2011-2014. E' co-editor di Bayesian Analysis

Ha fatto parte del comitato scientifico e organizzatore di numerosi convegni scientifici internazionali, fra cui la serie di workshop su "Bayesian nonparametrics" e "Bayesian Inference for Stochastic Processes" (BISP).

Ha ottenuto i riconoscimenti "Indennità di eccellenza nella ricerca" dell'Università Bocconi per il 2002 e 2003, e per il 2008 e 2009, e  "Research profile" 2010-2011-2012-2013.


Aree di interesse scientifico

Statistica Bayesiana: fondamenti, metodi e modelli, applicazioni.  Metodi bayesiani nonparametrici. Modelli  a variabili latenti. Modelli predittivi.  Modelli per sistemi dinamici e state space models.  


Pubblicazioni principali

 Petrone, S., Rousseau, J and Scricciolo, C. (2013) Bayes and Empirical Bayes: do they merge?. Biometrika, to appear.      Wade, S., Dunson, D. Petrone, S. and Trippa, L. (2013). Improving Prediction from Dirichlet Process Mixtures via Enrichment. Journal of Machine Learning Research, to appear.     Wade, S., Walker, G.W. and Petrone, S. (2013). A predictive study of Dirichlet Process Mixture Models for curve fitting. Scandinavian Journal of Statistics, to appear.      Fortini, S. and Petrone, S. (2012). Hierarchical Reinforced Urn Processes. Statistics and Probability Letters, 82, 1521-1529.     Fortini, S. and Petrone, S. (2012). Predictive construction of priors in Bayesian nonparametrics. Brazilian Journal of Probability and Statistics, 26, 423-449.   Petris, G. and Petrone, S. (2011) State space models in R. Journal of Statistical Software.   Wade, S., Mongelluzzo, S. and Petrone, S. (2010). Enriched conjugate priors for Bayesian nonparametric inference. Bayesian Analysis, 6, 359-386.       Petrone, S., Guindani, M. and Gelfand, A.E. (2009) "Hybrid Dirichlet mixture models for functional data", Journal Royal Statistical Society, Ser. B, 71, 755-782.      Petris, G., Petrone, S. and Campagnoli, P. (2009) Dynamic linear models with R, Springer, N.Y..    Trippa, L., Bulla, P. and Petrone, S. (2011) "Extended Bernstein prior via reinforced urn processes", Annals of the Institute of Statistical Mathematics,63, 481-469 (online 2009).    Petrone, S. and Veronese, P.  "Feller operators and mixture priors in Bayesian nonparametrics" (Statistica Sinica, 2010, 20, 379-404 ).     N.L.Hjort and Petrone, S. (2007) "Nonparametric quantile inference with Dirichlet processes", in: Advances in Statistical Modeling and Inference. Essays in Honor of Kjell A Doksum,  V. Nair Ed., 463-492.    A.E. Gelfand, M. Guindani and Petrone, S. (2007) "Bayesian nonparametric modelling for spatial data using Dirichlet processes" (with discussion), in: Bayesian Statistics 8, J.M. Bernardo, J.O. Berger, Dawid, A.P. and A.F.M. Smith Eds, Oxford University Press. Petrone, S. (2007) Discussion on "Approximating interval hypothesis: p-values and Bayes factors", by J. Russeau; in:  Bayesian Statistics 8, J.M. Bernardo, J.O.Berger, Dawid, A.P. and A.F.M. Smith Eds., Oxford University Press. Petrone, S. (2003) "A predictive point of view  on Bayesian nonparametrics"; in: Highly Structured Stochastic Systems, P. Green, N. Hjort and S. Richardson Eds, Oxford University Press. Petrone, S. and Wasserman, L. (2002) Consistency of Bernstein Polynomial Density Estimators, Journal of the Royal Statistical Society, Ser. B, 64, 79-100; Petrone, S. and Veronese, P. (2002) Non Parametric Mixture Priors Based on an Exponential Random Scheme, Statistical Methods and Applications, 11, 1-20. Campagnoli, P., Muliere,P. and Petrone, S. (2001) Generalized Dynamic Linear Models for Financial Time Series, Applied Stochastic Models in Business and Industry, 17, 27-39; Petrone, S. and Corielli, F. (2005) Dynamic Regression Using Bernstein Polynomials with Application to Estimation of the Term Structure of Interest Rates Studi Statistici 61, IMQ, Università Bocconi;  Petrone, S. (1999) Random Bernstein Polynomials, Scandinavian Journal of Statistics , 26, 373-393; Petrone, S. (1999)  Bayesian Density Estimation Using Bernstein Polynomials, Canadian Journal of Statistics, 27, 105-126; Petrone, S. (1999) Discussion on "Bayesian nonparametric inference for random distributions and related functions", by Walker, S.G., Damien, P., Laud, P.W., Smith, A.F.M., Journal of the Royal  Statistics Society, Ser. B, 61, 522-523. Petrone, S., Roberts, G.O. and Rosenthal, J.S. (1999) A Note on Convergence Rates of Gibbs Sampling for Nonparametric Mixtures, Far East Journal of Theoretical Statistics, 3, 213-225; Petrone, S. and Raftery, A.E. (1997) A Note on the Dirichlet Process Prior in Bayesian Nonparametric Inference with Partial Exchangeability, Statistics and Probability Letters, 36, 69-83. Mira, A. and Petrone, S. (1996) Bayesian Hierarchical Nonparametric Inference for Change-point Problems; in: J.M. Bernardo, J.O. Berger, A.P. Dawid, A.F.M. Smith (eds.), Bayesian Statistics 5, Oxford University Press, 693-703. Muliere, P. and Petrone, S. (1993) A Bayesian predictive approach to sequential searching for  an optimal dose: parametric and nonparametric models, Journal of the  Italian Statistical Society, 3, 349-364. Muliere, P. and Petrone, S. (1992) Generalized Lorenz curve and monotone  dependence orderings, Metron, L, 19-38.

 


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