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,Niccolo'Grieco,FrancescaIeva,AnnaMariaPaganoniand PiercesareSecchi...41 Bootstrapalgorithmsforvarianceestimationin PSsampling AlessandroBarbieroandFulviaMecatti...5 7 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) FrancescaIeva Naples,Italy MOX-DepartmentofMathematics PolitecnicodiMilano ChiaraRomualdi Milan,Italy DepartmentofBiology UniversityofPadova LuigiIppoliti Padova,Italy DepartmentofQuantitativeMethods andEconomicTheory H?avardRue UniversityG. d'Annunzio DepartmentofMathematicalSciences Chieti-Pescara,Italy NorwegianUniversityforScience andTechnology SaraMartino Trondheim,Norway DepartmentofMathematicalSciences NorwegianUniversityforScience PiercesareSecchi andTechnology MOX-DepartmentofMathematics Trondheim,Norway PolitecnicodiMilano Milan,Italy M. So?aMassa DepartmentofStatisticalSciences PaoloVidoni UniversityofPadova DepartmentofStatistics Padova,Italy UniversityofUdine Udine,Italy FulviaMecatti DepartmentofStatistics G.

The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.