01209nam--2200409---450-99000578222020331620121120143016.0978-88-6657-112-4000578222USA01000578222(ALEPH)000578222USA0100057822220121120d2012----km-y0itay50------baitaIT||||||||001yyCodice di procedura civileannotato con la giurisprudenzaAntonio Lombardi3. ed.RomaNel diritto2012X, 1931 p.25 cm<<I>> codici superiori2001<<I>> codici superiori2001001-------2001347.450502632LOMBARDI,Antonio382130ITsalbcISBD990005782220203316XXVII.1.A. 18974942 G.XXVII.1.A.00317556BKGIUFIORELLA9020121120USA011426FIORELLA9020121120USA011430PATRY9020130502USA011037PATRY9020130502USA011038Codice di procedura civile1074489UNISA05419nam 22008895 450 99646604480331620230816215150.03-642-33191-210.1007/978-3-642-33191-6(CKB)3390000000030230(SSID)ssj0000745818(PQKBManifestationID)11433380(PQKBTitleCode)TC0000745818(PQKBWorkID)10852957(PQKB)11537899(DE-He213)978-3-642-33191-6(MiAaPQ)EBC3070898(PPN)16832380X(EXLCZ)99339000000003023020120821d2012 u| 0engurnn#008mamaatxtccrAdvances in Visual Computing[electronic resource] 8th International Symposium, ISVC 2012, Rethymnon, Crete, Greece, July 16-18, 2012, Revised Selected Papers, Part II /edited by George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Fowlkes Charless, Wang Sen, Choi Min-Hyung, Stephan Mantler, Jurgen Schulze, Daniel Acevedo, Klaus Mueller, Michael Papka1st ed. 2012.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2012.1 online resource (XXIX, 771 p. 433 illus.)Image Processing, Computer Vision, Pattern Recognition, and Graphics ;7432Bibliographic Level Mode of Issuance: Monograph3-642-33190-4 Includes bibliographical references and index.Part II (LNCS 7432): unconstrained biometrics: advances and trends -- intelligent environments: algorithms and applications -- applications -- virtual reality; face processing and recognition.The two volume set LNCS 7431 and 7432 constitutes the refereed proceedings of the 8th International Symposium on Visual Computing, ISVC 2012, held in Rethymnon, Crete, Greece, in July 2012. The 68 revised full papers and 35 poster papers presented together with 45 special track papers were carefully reviewed and selected from more than 200 submissions. The papers are organized in topical sections: Part I (LNCS 7431) comprises computational bioimaging; computer graphics; calibration and 3D vision; object recognition; illumination, modeling, and segmentation; visualization; 3D mapping, modeling and surface reconstruction; motion and tracking; optimization for vision, graphics, and medical imaging, HCI and recognition. Part II (LNCS 7432) comprises topics such as unconstrained biometrics: advances and trends; intelligent environments: algorithms and applications; applications; virtual reality; face processing and recognition.Image Processing, Computer Vision, Pattern Recognition, and Graphics ;7432Pattern recognitionComputer graphicsOptical data processingUser interfaces (Computer systems)Application softwareBioinformaticsPattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XComputer Graphicshttps://scigraph.springernature.com/ontologies/product-market-codes/I22013Image Processing and Computer Visionhttps://scigraph.springernature.com/ontologies/product-market-codes/I22021User Interfaces and Human Computer Interactionhttps://scigraph.springernature.com/ontologies/product-market-codes/I18067Information Systems Applications (incl. Internet)https://scigraph.springernature.com/ontologies/product-market-codes/I18040Computational Biology/Bioinformaticshttps://scigraph.springernature.com/ontologies/product-market-codes/I23050Pattern recognition.Computer graphics.Optical data processing.User interfaces (Computer systems).Application software.Bioinformatics.Pattern Recognition.Computer Graphics.Image Processing and Computer Vision.User Interfaces and Human Computer Interaction.Information Systems Applications (incl. Internet).Computational Biology/Bioinformatics.006.4Bebis Georgeedthttp://id.loc.gov/vocabulary/relators/edtBoyle Richardedthttp://id.loc.gov/vocabulary/relators/edtParvin Bahramedthttp://id.loc.gov/vocabulary/relators/edtKoracin Darkoedthttp://id.loc.gov/vocabulary/relators/edtFowlkes Charless Wedthttp://id.loc.gov/vocabulary/relators/edtSen Wangedthttp://id.loc.gov/vocabulary/relators/edtMin-Hyung Choiedthttp://id.loc.gov/vocabulary/relators/edtMantler Stephanedthttp://id.loc.gov/vocabulary/relators/edtSchulze Jurgenedthttp://id.loc.gov/vocabulary/relators/edtAcevedo Danieledthttp://id.loc.gov/vocabulary/relators/edtMueller Klausedthttp://id.loc.gov/vocabulary/relators/edtPapka Michaeledthttp://id.loc.gov/vocabulary/relators/edtBOOK996466044803316Advances in Visual Computing772261UNISA05616nam 2200745Ia 450 991082446120332120200520144314.097866121232219781282123229128212322X978047074053804707405319780470740545047074054X(CKB)1000000000748754(EBL)437418(OCoLC)427565663(SSID)ssj0000239023(PQKBManifestationID)11175361(PQKBTitleCode)TC0000239023(PQKBWorkID)10238829(PQKB)11709323(MiAaPQ)EBC437418(Au-PeEL)EBL437418(CaPaEBR)ebr10307342(CaONFJC)MIL212322(Perlego)2774259(EXLCZ)99100000000074875420090227d2009 uy 0engur|n|---|||||txtccrRobust methods in biostatistics /Stephane Heritier ... [et al.]1st ed.Chichester, West Sussex ;Hoboken J. Wiley20091 online resource (294 p.)Wiley Series in Probability and Statistics ;v.825Description based upon print version of record.9780470027264 0470027266 Includes bibliographical references and index.Robust Methods in Biostatistics; Contents; Preface; Acknowledgments; 1 Introduction; What is Robust Statistics?; Against What is Robust Statistics Robust?; Are Diagnostic Methods an Alternative to Robust Statistics? .; How do Robust Statistics Compare with Other Statistical Procedures in Practice?; 2 Key Measures and Results; Introduction; Statistical Tools for Measuring Robustness Properties; The Influence Function; The Breakdown Point; Geometrical Interpretation; The Rejection Point; General Approaches for Robust Estimation; The General Class of M-estimators; Properties of M-estimatorsThe Class of S-estimatorsStatistical Tools for Measuring Tests Robustness; Sensitivity of the Two-sample t-test; Local Stability of a Test: the Univariate Case; Global Reliability of a Test: the Breakdown Functions; General Approaches for Robust Testing; Wald Test, Score Test and LRT; Geometrical Interpretation; General -type Classes of Tests; Asymptotic Distributions; Robustness Properties; 3 Linear Regression; Introduction; Estimating the Regression Parameters; The Regression Model; Robustness Properties of the LS and MLE Estimators; Glomerular Filtration Rate (GFR) Data ExampleRobust EstimatorsGFR Data Example (continued); Testing the Regression Parameters; Significance Testing; Diabetes Data Example; Multiple Hypothesis Testing; Diabetes Data Example (continued); Checking and Selecting the Model; Residual Analysis; GFR Data Example (continued); Diabetes Data Example (continued); Coefficient of Determination; Global Criteria for Model Comparison; Diabetes Data Example (continued); Cardiovascular Risk Factors Data Example; 4 Mixed Linear Models; Introduction; The MLM; The MLM Formulation; Skin Resistance Data; Semantic Priming Data; Orthodontic Growth DataClassical Estimation and InferenceMarginal and REML Estimation; Classical Inference; Lack of Robustness of Classical Procedures; Robust Estimation; Bounded Influence Estimators; S-estimators; MM-estimators; Choosing the Tuning Constants; Skin Resistance Data (continued); Robust Inference; Testing Contrasts; Multiple Hypothesis Testing of the Main Effects; Skin Resistance Data Example (continued); Semantic Priming Data Example (continued); Testing the Variance Components; Checking the Model; Detecting Outlying and Influential Observations; Prediction and Residual Analysis; Further ExamplesMetallic Oxide DataOrthodontic Growth Data (continued); Discussion and Extensions; 5 Generalized Linear Models; Introduction; The GLM; Model Building; Classical Estimation and Inference for GLM; Hospital Costs Data Example; Residual Analysis; A Class of M-estimators for GLMs; Choice of ψ and w(x); Fisher Consistency Correction; Nuisance Parameters Estimation; IF and Asymptotic Properties; Hospital Costs Example (continued); Robust Inference; Significance Testing and CIs; General Parametric Hypothesis Testing and Variable Selection; Hospital Costs Data Example (continued)Breastfeeding Data ExampleRobust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robustWiley Series in Probability and StatisticsBiometryStatistical methodsBiomathematicsBiometryStatistical methods.Biomathematics.570.1/5195570.15195Heritier Stephane432026MiAaPQMiAaPQMiAaPQBOOK9910824461203321Robust methods in biostatistics3927291UNINA