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Complex Data Modeling and Computationally Intensive Statistical Methods / edited by Pietro Mantovan, Piercesare Secchi
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Contributions to Statistics | SpringerLink BücherPublisher: Milano : Springer-Verlag Milan, 2010Description: Online-Ressource (X, 170p, digital)ISBN:- 9788847013865
- 1283076349
- 9781283076340
- 519.5
- 004 23
- 519.50285
- QA276-280
- QA76.9.D26
Contents:
Summary: 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.Summary: 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.PPN: PPN: 1650767854Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SXMS | ZDB-2-SMA
Title Page; Copyright Page; Preface; Table of Contents; List of Contributors; Space-time texture analysis in thermal infraredimaging for classification of Raynaud's Phenomenon; Mixed-effects modelling of Kevlar fibre failure timesthrough Bayesian non-parametrics; Space filling and locally optimal designs for Gaussian Universal Kriging; Exploitation, integration and statistical analysis of thePublic Health Database and STEMI Archive in theLombardia 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 processesCase studies in Bayesian computation using INLA; A graphical models approach for comparing gene sets; A graphical models approach for comparing gene sets; Predictive densities and prediction limits based onpredictive likelihoods; Computer-intensive conditional inference; Monte Carlo simulation methods for reliability estimation and failure prognostics
CoverPreface -- Table of Contents -- List of Contributors -- Space-Time Texture Analysis in Thermal Infraredimaging for Classification of Raynauds Phenomenon -- 1 Introduction -- 2 Thedata -- 3 Processing Thermal High Resolution Infrared Images -- 3.1 Segmentation -- 3.2 Registration -- 4 Feature Extraction -- 4.1 St-Gmrfs -- 4.2 Texture Statistics Through Co-Occurrence Matrices -- 5 Classification Results -- 6 Conclusions -- References -- Mixed-Effects Modelling of Kevlar Fibre Failure Timesthrough Bayesian Non-Parametrics -- 1 Introduction -- 2 Accelerated Life Models for Kevlar Fibre Life Data -- 3 The Bayesian Semiparametric Aft Model -- 4 Data Analysis -- 5 Conclusions -- Appendix -- References -- Space Filling and Locally Optimal Designs for Gaussian Universal Kriging -- 1 Introduction -- 2 Kriging Methodology -- 3 Optimality of Space Filling Designs -- 4 Locally Optimal Designs for Universal Kriging -- 4.1 Optimal Designs for Estimation -- 4.2 Optimal Designs for Prediction -- 5 Conclusions -- References -- Exploitation, Integration and Statistical Analysis of Thepublic Health Database and Stemi Archive in Thelombardia Region -- 1 Introduction -- 2 The Momi2 Study -- 3 The Stemi Archive -- 4 The Public Health Database -- 4.1 Healthcare Databases -- 4.2 Health Information Systems in Lombardia -- 5 The Statistical Perspective -- 5.1 Frailty Models -- 5.2 Generalised Linear Mixed Models -- 5.3 Bayesian Hierarchical Models -- 6 Conclusions -- References -- Bootstrap Algorithms for Variance Estimation in PsSampling -- 1 Introduction -- 2 The Na239;ve Boostrap -- 3 Holmbergs PsBootstrap -- 4 The 0.5 Ps-Bootstrap -- 5 The X-Balanced Ps-Bootstrap -- 6 Simulation Study -- 7 Conclusions -- References -- Fast Bayesian Functional Data Analysis of Basal Body Temperature -- 1 Introduction -- 2 Methods -- 2.1 Rvm in Linear Models -- 2.2 Extension to Linear Mixed Model -- 3 Results: Application to Bbt Data -- 3.1 Subject-Specific Profiles -- 3.2 Subject-Specific and Population Average Profiles -- 3.3 Prediction -- 4 Conclusions -- References -- A Parametric Markov Chain to Model Age- and State-Dependent Wear Processes -- 1 Introduction -- 2 System Description and Preliminary Technological Considerations -- 3 Data Description and Preliminary Statistical Considerations -- 4 Model Description -- 5 Parameter Estimation -- 6 Testing Dependence on Time and/or State -- 7 Conclusions -- References -- Case Studies in Bayesian Computation Using Inla -- 1 Introduction -- 2 Latent Gaussian Models -- 3 Integrated Nested Laplace Approximation -- 4 The Inla Package for R -- 5 Case Studies -- 5.1 A Glmm With Over-Dispersion -- 5.2 Childhood Under Nutrition in Zambia: Spatial Analysis -- 5.3 A Simple Example of Survival Data Analysis -- 6 Conclusions -- References -- A Graphical Models Approach for Comparing Gene Sets -- 1 Introduction -- 2 Latent Gaussian Models -- 3 Integ.
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