Complex Data Modeling and Computationally Intensive Statistical Methods

ISBN-13: 9788847013858 / Angielski / Twarda / 2010 / 164 str.

Pietro Mantovan;Piercesare Secchi

Opis

Recentyearshaveseentheadventanddevelopmentofmanydevicesabletorecordand storeaneverincreasingamountofinformation. Thefastprogressofthesetechnologies is ubiquitousthroughoutall ?elds of science and applied contexts, ranging from medicine, biologyandlifesciences, toeconomicsandindustry. Thedataprovided bytheseinstrumentshavedifferentforms:2D-3Dimagesgeneratedbydiagnostic medicalscanners, computervisionorsatelliteremotesensing, microarraydataand genesets, integratedclinicalandadministrativedatafrompublichealthdatabases, realtimemonitoringdataofabio-marker, systemcontroldatasets. Allthesedata sharethecommoncharacteristicofbeingcomplexandoftenhighlydimensional. Theanalysisofcomplexandhighlydimensionaldataposesnewchallengesto thestatisticianandrequiresthedevelopmentofnovelmodelsandtechniques, fueling manyfascinatingandfastgrowingresearchareasofmodernstatistics. Anincomplete listincludes for example: functionaldata analysis, that deals with data having a functionalnature, suchascurvesandsurfaces;shapeanalysisofgeometricforms, that relatestoshapematchingandshaperecognition, appliedtocomputationalvisionand medicalimaging;datamining, thatstudiesalgorithmsfortheautomaticextraction ofinformationfromdata, elicitingrulesandpatternsoutofmassivedatasets;risk analysis, fortheevaluationofhealth, environmental, andengineeringrisks;graphical models, thatallowproblemsinvolvinglarge-scalemodelswithmillionsofrandom variableslinkedincomplexwaystobeapproached;reliabilityofcomplexsystems, whoseevaluationrequirestheuseofmanystatisticalandprobabilistictools;optimal designofcomputersimulationstoreplaceexpensiveandtimeconsumingphysical experiments. Thecontributionspublishedinthisvolumearetheresultofaselectionbasedonthe presentations(aboutonehundred)givenattheconference S. Co. 2009: Complexdata 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, NiccoloGrieco, 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 NiccoloGrieco BusinessandStatistics A. O. NiguardaCaGranda 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) FrancescaIeva Naples, Italy MOX DepartmentofMathematics PolitecnicodiMilano ChiaraRomualdi Milan, Italy DepartmentofBiology UniversityofPadova LuigiIppoliti Padova, Italy Department

Recenzje

From the reviews:

"This volume will be useful for the researchers working in this area. I read a few papers and, all in all, the book seems to have good applications. ... All the papers are well structured and consistent in style and presentations. Each paper begins with an abstract and ends with a list of references. ... The volume offers a host of computer intensive techniques and applications, and a number of statistical models dealing with complex and high-dimensional data-related problems." (Technometrics, Vol. 54 (1), February, 2012)

Spis treści

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.

O autorze

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.

Blurb

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.