Complex Data Modeling and Computationally Intensive Statistical Methods / Edition 1

Complex Data Modeling and Computationally Intensive Statistical Methods / Edition 1

Complex Data Modeling and Computationally Intensive Statistical Methods / Edition 1
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ISBN-10:
8847013852
ISBN-13:
9788847013858
Pub. Date:
11/04/2010
Publisher:
Springer Milan
ISBN-10:
8847013852
ISBN-13:
9788847013858
Pub. Date:
11/04/2010
Publisher:
Springer Milan
Complex Data Modeling and Computationally Intensive Statistical Methods / Edition 1

Complex Data Modeling and Computationally Intensive Statistical Methods / Edition 1

Overview

The last years have seen the advent and development of many devices able to record and store an always increasing amount of complex and high dimensional data; 3D images generated by medical scanners or satellite remote sensing, DNA microarrays, real time financial data, system control datasets, ....

The analysis of this data poses new challenging problems and requires the development of novel statistical models and computational methods, fueling many fascinating and fast growing research areas of modern statistics. The book offers a wide variety of statistical methods and is addressed to statisticians working at the forefront of statistical analysis.

Product Details

ISBN-13: 9788847013858
Publisher: Springer Milan
Publication date: 11/04/2010
Series: Contributions to Statistics
Edition description: 2010
Pages: 164
Product dimensions: 6.40(w) x 9.30(h) x 0.70(d)

About the Author

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 shastic 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.

Table of Contents

Space-time texture analysis in thermal infrared imaging for classification of Raynaud's Phenomenon Graziano Aretusi Lara Fontanella Luigi Ippoliti Arcangelo Merla 1

Mixed-effects modelling of Kevlar fibre failure times through Bayesian non-parametrics Raffaele Argiento Alessandra Guglielmi Antonio Pievatolo 13

Space filling and locally optimal designs for Gaussian Universal Kriging Alessandro Baldi Antognini Maroussa Zagoraiou 27

Exploitation, integration and statistical analysis of the Public Health Database and STEMI Archive in the Lombardia region Pietro Barbieri Niccoló Grieco Francesca leva Anna Maria Paganoni Piercesare Secchi 41

Bootstrap algorithms for variance estimation in πPS sampling Alessandro Barbiero Fulvia Mecatti 57

Fast Bayesian functional data analysis of basal body temperature James M. Ciera 71

A parametric Markov chain to model age- and state-dependent wear processes Massimiliano Giorgio Maurizio Guida Gianpaolo Pulcini 85

Case studies in Bayesian computation using INLA Sara Martino Havard Rue 99

A graphical models approach for comparing gene sets M. Sofia Massa Monica Chiogna Chiara Romualdi 115

Predictive densities and prediction limits based on predictive likelihoods Paolo Vidoni 123

Computer-intensive conditional inference G. Alastair Young Thomas J. DiCiccio 137

Monte Carlo simulation methods for reliability estimation and failure prognostics Enrico Zio 151

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