Complex Data Modeling and Computationally Intensive Statistical Methods

17.09.2010

Englisch

9788847013858

Springer Italia

Contributions to Statistics

Nein

Pietro Mantovan, Piercesare Secchi

164

Gebundene Ausgabe

Pietro Mantovan has been Professor of Statistics since 1986 at the University Ca' Foscari of Venezia, Italy, where he has served as coordinator of research units, head of the Departement of Statistics, and Dean of the Faculty of Economics. He has written several articles, monographs and textbooks on classical and Bayesian methods for statistical inference. His recent research interests focus on Bayesian methods for learning and prediction, statistical perturbation models for matrix data, dynamic regression with covariate errors, parallel algorithms for system identification in dynamic models, on line monitoring and forecasting of environmental data, hydrological forecasting uncertainty assessment, and robust inference processes.Piercesare Secchi is Professor of Statistics at MOX since 2005 and Director of the Department of Mathematics at the Politecnico di Milano. He got a Doctorate in Methodological Statistics from the University of Trento in 1992 and a PhDin Statistics from the University of Minnesota in 1995. He has written several papers on stochastic games and on Bayesian nonparametric predictive inference and bootstrap techniques. His present research interests focus on statistical methods for the exploration, classification and analysis of high dimensional data, like functional data or images generated by medical diagnostic devices or by remote sensing. He also works on models for Bayesian inference, in particular those generated by urn schemes, on response adaptive designs of experiments for clinical trials and on biodata mining. He is PI of different projects in applied statistics and coordinator of the Statistical Unit of the Aneurisk project.

Likelihood, STATISTICA, Time series, Variance, bayesian statistics, biodata mining, classification, classification and prediction of high dimensional data, complex data surveys, computational methods for statistics, data analysis, data mining, machine learning, statistical methods for industry and technology, statistics

UFM - Mathematische und statistische Software PBT - Wahrscheinlichkeitsrechnung und Statistik UNF - Data Mining UYQE - Wissensbasierte Systeme, Expertensysteme

2410 mm

160 cm