Complex Data Modeling and Computationally Intensive Statistical Methods

Pietro Mantovan

ISBN 10: 8847013852 / ISBN 13: 9788847013858

Editore: Springer, Springer Sep 2010, 2010

Riguardo questo articolo

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 176 pp. Englisch.

Codice articolo 9788847013858

Dati bibliografici

• Titolo: Complex Data Modeling and Computationally Intensive Statistical Methods
• Editore: Springer, Springer Sep 2010
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 8847013852
• ISBN 13: 9788847013858
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 15 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 15 millimetri di profondità
• Peso: 436 grammi

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Riassunto

modelingandcomputationallyintensivemethodsforestimationandprediction ,held ? atthePolitecnicodiMilano. S. Co. isaforumforthediscussionofnewdevelopments ? September14 16,2009. Thatof2009isitssixthedition,the?rstonebeingheldinVenice in1999. VI Preface andapplicationsofstatisticalmethodsandcomputationaltechniquesforcomplexand highlydimensionaldatasets. Thebookisaddressedtostatisticiansworkingattheforefrontofthestatistical analysisofcomplexandhighlydimensionaldataandoffersawidevarietyofstatistical models,computerintensivemethodsandapplications. Wewishtothankallassociateeditorsandrefereesfortheirvaluablecontributions thatmadethisvolumepossible. MilanandVenice,May2010 PietroMantovan PiercesareSecchi Contents Space-timetextureanalysisinthermalinfraredimagingforclassi?cation ofRaynaud sPhenomenon GrazianoAretusi,LaraFontanella,LuigiIppolitiandArcangeloMerla. . . . . . 1 Mixed-effectsmodellingofKevlar?brefailuretimesthroughBayesian non-parametrics RaffaeleArgiento,AlessandraGuglielmiandAntonioPievatolo. . . . . . . . . . . . 13 Space?llingandlocallyoptimaldesignsforGaussianUniversalKriging AlessandroBaldiAntogniniandMaroussaZagoraiou. . . . . . . . . . . . . . . . . . . . 27 Exploitation,integrationandstatisticalanalysisofthePublicHealth DatabaseandSTEMIArchiveintheLombardiaregion PietroBarbieri,Niccolo`Grieco,FrancescaIeva,AnnaMariaPaganoniand PiercesareSecchi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Bootstrapalgorithmsforvarianceestimationin PSsampling AlessandroBarbieroandFulviaMecatti. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 FastBayesianfunctionaldataanalysisofbasalbodytemperature JamesM. Ciera. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 AparametricMarkovchaintomodelage-andstate-dependentwear processes MassimilianoGiorgio,MaurizioGuidaandGianpaoloPulcini. . . . . . . . . . . . . 85 CasestudiesinBayesiancomputationusingINLA SaraMartinoandHav ? ardRue. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Agraphicalmodelsapproachforcomparinggenesets M. So?aMassa,MonicaChiognaandChiaraRomualdi. . . . . . . . . . . . . . . . . . 115 VIII Contents Predictivedensitiesandpredictionlimitsbasedonpredictivelikelihoods PaoloVidoni. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Computer-intensiveconditionalinference G. AlastairYoungandThomasJ. DiCiccio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 MonteCarlosimulationmethodsforreliabilityestimationandfailure prognostics EnricoZio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 ListofContributors AlessandroBaldiAntognini JamesM. Ciera DepartmentofStatisticalSciences DepartmentofStatisticalSciences UniversityofBologna UniversityofPadova Bologna,Italy Padova,Italy ThomasJ. DiCiccio GrazianoAretusi DepartmentofSocialStatistics DepartmentofQuantitativeMethods CornellUniversity andEconomicTheory Ithaca,USA UniversityG. d Annunzio Chieti-Pescara,Italy LaraFontanella DepartmentofQuantitativeMethods RaffaeleArgiento andEconomicTheory CNRIMATI UniversityG. d Annunzio Milan,Italy Chieti-Pescara,Italy MassimilianoGiorgio PietroBarbieri DepartmentofAerospace Uf?cioQualita` andMechanicalEngineering CernuscosulNaviglio,Italy SecondUniversityofNaples Aversa(CE),Italy AlessandroBarbiero DepartmentofEconomics Niccolo`Grieco BusinessandStatistics A. O. NiguardaCa`Granda UniversityofMilan Milan,Italy Milan,Italy MaurizioGuida MonicaChiogna DepartmentofElectrical DepartmentofStatisticalSciences andInformationEngineering UniversityofPadova UniversityofSalerno Padova,Italy Fisciano(SA),Italy X ListofContributors AlessandraGuglielmi AntonioPievatolo DepartmentofMathematics CNRIMATI PolitecnicodiMilano Milan,Italy Milan,Italy GianpaoloPulcini alsoaf?liatedtoCNRIMATI,Milano IstitutoMotori NationalResearchCouncil(CNR

Informazioni sull?autore

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 PhD in Statistics from the University of Minnesota in 1995. He has written several papers on stochastic 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.