Complex Data Modeling and Computationally Intensive Statistical Methods

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Mantovan, Pietro (Επιμελητής έκδοσης), Secchi, Piercesare (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Milano : Springer Milan, 2010.
• Σειρά: Contributions to Statistics,
• Θέματα: Mathematics.; Data mining.; Computer software.; Statistics.; Mathematical Software.; Statistics and Computing/Statistics Programs.; Statistical Theory and Methods.; Data Mining and Knowledge Discovery.

 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. 

 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.