Complex Data Modeling and Computationally Intensive Statistical Methods

Pietro Mantovan

ISBN 10: 8847013852 / ISBN 13: 9788847013858

Edité par Springer, Springer, 2010

A propos de cet article

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.

N° de réf. du vendeur 9788847013858

Détails bibliographiques

• Titre: Complex Data Modeling and Computationally Intensive Statistical Methods
• Éditeur: Springer, Springer
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 8847013852
• ISBN à 13 chiffres: 9788847013858
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 241 L x 160 H x 15 P (en millimètres) 241 millimètres de largeur par 160 millimètres de hauteur par 15 millimètres de profondeur
• Poids: 436 grammes

A propos de ce titre

Synopsis

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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 FastBayesianfunctionaldataanalysisofbasalbodytemperature JamesM. Ciera. . . . . . . . . . . . . . . . . . . . . . . .

À propos de l?auteur

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 PhDin 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.