Description
Complex Data Modeling and Computationally Intensive Statistical Methods, Softcover reprint of the original 1st ed. 2010
Contributions to Statistics Series
Authors: Mantovan Pietro, Secchi Piercesare
Language: EnglishSubjects for Complex Data Modeling and Computationally Intensive...:
Keywords
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
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Add to cart the print on demand of Mantovan Pietro, Secchi PiercesarePublication date: 08-2016
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Add to cart the book of Mantovan Pietro, Secchi PiercesarePublication date: 09-2010
164 p. · Paperback
Description
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/li>Biography
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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.