Complex Data Modeling and Computationally Intensive Statistical Methods

Editors: Pietro Mantovan, Piercesare Secchi



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.

Table of contents (12 chapters)

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 πPS 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