Complex Data Modeling and Computationally Intensive Statistical Methods

Pietro Mantovan

ISBN 10: 8847058066 / ISBN 13: 9788847058064

Editore: Springer, Springer Aug 2016, 2016

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This item is printed on demand - Print on Demand Titel. Neuware -Space-time texture analysis in thermal infrared imaging for classification of Raynaud's Phenomenon.- Mixed-effects modelling of Kevlar fibre failure times through Bayesian non-parametrics.- Space filling and locally optimal designs for Gaussian Universal Kriging.- Exploitation, integration and statistical analysis of the Public Health Database and STEMI Archive in the Lombardia region.- Bootstrap algorithms for variance estimation in PS sampling.- Fast Bayesian functional data analysis of basal body temperature.- A parametric Markov chain to model age- and state-dependent wear processes.- Case studies in Bayesian computation using INLA.- A graphical models approach for comparing gene sets.- Predictive densities and prediction limits based on predictive likelihoods.- Computer-intensive conditional inference.- Monte Carlo simulation methods for reliability estimation and failure prognostics.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 176 pp. Englisch.

Codice articolo 9788847058064

Dati bibliografici

• Titolo: Complex Data Modeling and Computationally Intensive Statistical Methods
• Editore: Springer, Springer Aug 2016
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 8847058066
• ISBN 13: 9788847058064
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 10 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 10 millimetri di profondità
• Peso: 277 grammi

Descrizione articolo

Riassunto

Selected from the conference "S.Co.2009: Complex Data Modeling and Computationally Intensive Methods for Estimation and Prediction," these 20 papers cover the latest in statistical methods and computational techniques for complex and high dimensional datasets.

Informazioni sull?autore

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 PhD in 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.