Complex Data Modeling and Computationally Intensive Statistical Methods [Hardcover]

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.

Space-time texture analysis in thermal infrared imaging for classification of Raynauds 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.

From the reviews:

This volume will be useful for the researchers working in this area. I read a few papers and, all in all, the book seems to have good applications. & All the papers are well structured and consistent in style and presentations. Each paper begins with an abstract and ends with a list of references. & The volume offers a host of computer intensive techniques and applications, and a number of statistical models dealing with complex and high-dimensional data-related problems. (Technometrics, Vol. 54 (1), February, 2012)

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 olÈ