Complex Data Modeling and Computationally Intensive Statistical Methods

Pietro Mantovan

ISBN 10: 8847013852 / ISBN 13: 9788847013858

Editore: Springer, Springer, 2010

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - 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.

Codice articolo 9788847013858

Dati bibliografici

• Titolo: Complex Data Modeling and Computationally Intensive Statistical Methods
• Editore: Springer, Springer
• 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

Descrizione articolo

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.