LEADER 01824nam 2200397Ia 450 001 996397072503316 005 20210104171811.0 035 $a(CKB)4940000000063708 035 $a(EEBO)2240930511 035 $a(OCoLC)ocm82475498e 035 $a(OCoLC)82475498 035 $a(EXLCZ)994940000000063708 100 $a20070209d1633 uy 0 101 0 $alat 135 $aurbn||||a|bb| 200 10$aStanbrigii embryon relimatum, seu, Vocabularium metricum olim a? Iohanne Stanbrigio digestum$b[electronic resource] $edein a? Thoma Newtono aliquantulum repurgatum. Nunc vero locupletatum, defæcatum, legitimo nec non rotundo plerumque carmine exultans, & in majorem pueritiæ balbutientis usum undequaq[ue] accommodatum, extrema? opera? & industria Ioh. Brinslæi. 210 $aLondini $cExcusum typis T. Cotes, impensis Tho. Knight : & venales prostant a Tho. Alchorne in C?metrio Paulino, ad insigne viridis Draconis.$d1633. 215 $a[64] p 300 $aEnglish and Latin on facing pages. 300 $aPrinter's device (McK. 283) on t.p.; text head-pieces, initials. 300 $aSignatures: A-H?. 300 $aImperfect: stained, torn, and with print show-though. 300 $aReproduction of original in: Massachusetts Historical Society. 330 $aeebo-0089 606 $aLatin language$xTerms and phrases$yEarly works to 1800 615 0$aLatin language$xTerms and phrases 700 $aStanbridge$b John$f1463-1510.$0196826 701 $aNewton$b Thomas$f1542?-1607.$01001761 701 $aBrinsley$b John$ffl. 1581-1624.$01000975 801 0$bUMI 801 1$bUMI 801 2$bUMI 906 $aBOOK 912 $a996397072503316 996 $aStanbrigii embryon relimatum, seu, Vocabularium metricum olim a? Iohanne Stanbrigio digestum$92375128 997 $aUNISA LEADER 05446nam 2200697 a 450 001 9910830901103321 005 20170815112942.0 010 $a1-281-45055-3 010 $a9786611450557 010 $a1-118-26716-8 010 $a0-470-39274-6 035 $a(CKB)1000000000535894 035 $a(EBL)353534 035 $a(SSID)ssj0000253241 035 $a(PQKBManifestationID)11200339 035 $a(PQKBTitleCode)TC0000253241 035 $a(PQKBWorkID)10186143 035 $a(PQKB)11596341 035 $a(MiAaPQ)EBC353534 035 $a(CaSebORM)9780470243664 035 $a(OCoLC)773301503 035 $a(EXLCZ)991000000000535894 100 $a20080410d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aSubprime mortgage credit derivatives$b[electronic resource] /$fLaurie S. Goodman ... [et al.] 205 $a1st edition 210 $aHoboken, N.J. $cJohn Wiley & Sons$dc2008 215 $a1 online resource (352 p.) 225 1 $aThe Frank J. Fabozzi series 300 $aIncludes index. 311 $a0-470-24366-X 327 $aSubprime Mortgage Credit Derivatives; Contents; Preface; About the Authors; Part I: Mortgage Credit; Chapter 1: Overview of the Nonagency Mortgage Market; ISSUANCE VOLUMES; ROOTS OF THE 2007- 2008 SUBPRIME CRISIS; DEFINING CHARACTERISTICS OF NONAGENCY MORTGAGES; LOAN CHARACTERISTICS; RISK LAYERING; AGENCY VERSUS NONAGENCY EXECUTION; SUMMARY; Chapter 2: First Lien Mortgage Credit; CONCEPTS AND MEASUREMENTS OF MORTGAGE CREDIT; COLLATERAL CHARACTERISTICS AND MORTGAGE CREDIT: ASSAULT OF THE FOUR Cs IN 2006 ( CREDIT, COLLATERAL, CAPACITY, AND CHARACTER) 327 $aTHE END GAME: FORECLOSURE, REO TIMELINE, AND SEVERITYTHE ROLE OF UNOBSERVABLE IN 2006 SUBPRIME MORTGAGE CREDIT; Chapter 3: Second Lien Mortgage Credit; TWO TYPES OF SECONDS; HIGHER RISKS IN SECONDS; RECENT PERFORMANCE; WHY HIGHER LOSSES?; SUMMARY; Part II: Mortgage Securitizations; Chapter 4: Features of Excess Spread/Overcollateralization: The Principle Subprime Structure; EXCESS SPREAD-BASED CREDIT ENHANCEMENT; OC IN ALT-A-LAND; OC INTERNAL WORKINGS; SUMMARY; Chapter 5: Subprime Triggers and Step-Downs; THE STEP-DOWN AND THE TRIGGER; BBB STACK (ON THE KNIFE'S EDGE) 327 $aEFFECT OF TRIGGERS AND THE LOSS WATERLINESAMPLING THE SUBPRIME UNIVERSE; 2000- 2003 DEAL STEP-DOWN SUMMARY; STEP-DOWN AND CREDIT EFFECTS; SUMMARY; Part III: Credit Default Swaps on Mortgage Securities; Chapter 6: Introduction to Credit Default Swap on ABS CDS; CORPORATE CDS FUNDAMENTALS AND TERMINOLOGY; DIFFERENCES BETWEEN CORPORATE CDS AND ABS CDS; DIFFICULTIES IN ABS CDS; ABS CDS EFFECT ON ABS CDO MANAGEMENT; TWO NEW TYPES OF ABS CDOs; SUMMARY; Chapter 7: The ABX and TABX Indices; BACKGROUND; HOW A DEAL GETS INTO THE INDEX; INDEX MECHANICS; INDEX PRICING OVER TIME; ABX TRANCHE TRADING 327 $aTABX PRICINGTABX VERSUS CDOs; SUMMARY; Chapter 8: Relationship among Cash, ABCDS, and the ABX; FUNDAMENTAL CONTRACTUAL DIFFERENCES: SINGLE-NAME ABCDS/ABX INDEX/CASH; SUPPLY/DEMAND TECHNICALS; WHAT KEEPS THE ARBITRAGE FROM GOING AWAY?; SUMMARY; APPENDIX: IMPORTANCE OF ABCDS TO CDO MANAGERS; Chapter 9: Credit Default Swaps on CDOs; CDO CDS NOMENCLATURE; CDO CREDIT PROBLEMS AND THEIR CONSEQUENCES; ALTERNATIVE INTEREST CAP OPTIONS; MISCELLANEOUS TERMS; CASH CDO VERSUS CDO CDS; EXITING A CDO CDS; RATING AGENCY CONCERNS ON CDOs THAT SELL PROTECTION VIA CDO CDS; SUMMARY 327 $aPart IV: Loss Projection and Security ValuationChapter 10: Loss Projection for Subprime, Alt-A, and Second Lien Mortgages; TWO WAYS OF PROJECTING LOSS; DEFAULT TIMING; STEPS IN PREDICTING COLLATAL LOSSES; PROS AND CONS OF THE DEFAULT TIMING CURVE; HISTORICAL MODEL FIT VERSUS ACTUAL; DEFAULT TIMING IS NOT EQUAL TO LOSS TIMING; AN ALTERNATIVE SPECIFICATION; ALT-A AND CLOSED-END SECONDS; SUMMARY; Chapter 11: Valuing the ABX; REVIEW OF BASIC VALUATION FOR ABX INDICES; REVIEW OF VALUATION APPROACHES; ECONOMETRIC APPROACH; ABX VALUATION; THE "SIMPLE" OR DO-IT-YOURSELF APPROACH TO ABX VALUATION 327 $aABX AFTER SUBPRIME SHUTDOWN 330 $aMortgage credit derivatives are a risky business, especially of late. Written by an expert author team of UBS practitioners-Laurie Goodman, Shumin Li, Douglas Lucas, and Thomas Zimmerman-along with Frank Fabozzi of Yale University, Subprime Mortgage Credit Derivatives covers state-of-the-art instruments and strategies for managing a portfolio of mortgage credits in today's volatile climate.Divided into four parts, this book addresses a variety of important topics, including mortgage credit (non-agency, first and second lien), mortgage securitizations (alternate structures and su 410 0$aFrank J. Fabozzi series. 606 $aSubprime mortgage loans$zUnited States 606 $aSubprime mortgage loans$zUnited States$vStatistics 606 $aSecondary mortgage market$zUnited States 615 0$aSubprime mortgage loans 615 0$aSubprime mortgage loans 615 0$aSecondary mortgage market 676 $a332.63/244 676 $a332.63244 686 $a85.33$2bcl 700 $aGOODMAN$b LAURIE$01636036 701 $aGoodman$b Laurie S$01636037 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830901103321 996 $aSubprime mortgage credit derivatives$93977127 997 $aUNINA LEADER 09383nam 22008895 450 001 9910768481303321 005 20260320155944.0 010 $a3-031-09034-9 024 7 $a10.1007/978-3-031-09034-9 035 $a(MiAaPQ)EBC31016755 035 $a(Au-PeEL)EBL31016755 035 $a(DE-He213)978-3-031-09034-9 035 $a(OCoLC)1415895688 035 $a(CKB)29374735700041 035 $a(oapen)doab132010 035 $a(oapen)132010 035 $a(EXLCZ)9929374735700041 100 $a20231207d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aClassification and Data Science in the Digital Age /$fedited by Paula Brito, José G. Dias, Berthold Lausen, Angela Montanari, Rebecca Nugent 205 $a1st ed. 2023. 210 $aCham$cSpringer Nature$d2023 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (393 pages) 225 1 $aStudies in Classification, Data Analysis, and Knowledge Organization,$x2198-3321 311 08$aPrint version: Brito, Paula Classification and Data Science in the Digital Age Cham : Springer International Publishing AG,c2024 9783031090332 327 $aPreface -- R. Abdesselam: A Topological Clustering of Individuals -- C. Anton and I. Smith: Model Based Clustering of Functional Data with Mild Outliers -- F. Antonazzo and S. Ingrassia: A Trivariate Geometric Classification of Decision Boundaries for Mixtures of Regressions -- E. Arnone, E. Cunial, and L. M. Sangalli: Generalized Spatio-temporal Regression with PDE Penalization -- R. Ascari and S. Migliorati: A New Regression Model for the Analysis of Microbiome Data -- R. Aschenbruck, G. Szepannek, and A. F. X. Wilhelm: Stability of Mixed-type Cluster Partitions for Determination of the Number of Clusters -- A. Ashofteh and P. Campos: A Review on Official Survey Item Classification for Mixed-Mode Effects Adjustment -- V. Batagelj: Clustering and Blockmodeling Temporal Networks ? Two Indirect Approaches -- R. Boutalbi, L. Labiod, and M. Nadif: Latent Block Regression Model -- N. Chabane, M. Achraf Bouaoune, R. Amir Sofiane Tighilt, B. Mazoure, N. Tahiri, and V. Makarenkov: Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations -- T. Chadjipadelis and S. Magopoulou: COVID-19 Pandemic: a Methodological Model for the Analysis of Government?s Preventing Measures and Health Data Records -- J. Champagne Gareau, É. Beaudry, and V. Makarenkov: pcTVI: Parallel MDP Solver Using a Decomposition into Independent Chains -- C. Di Nuzzo and S. Ingrassia: Three-way Spectral Clustering -- J. Dob?a and H. A. L. Kiers: Improving Classification of Documents by Semi-supervised Clustering in a Semantic Space -- J. Gama: Trends in Data Stream Mining -- L. A. García-Escudero, A. Mayo-Iscar, G. Morelli, and M. Riani: Old and New Constraints in Model Based Clustering -- V. G Genova, G. Giordano, G . Ragozini, and M. Prosperina Vitale: Clustering Student Mobility Data in 3-way Networks -- R. Giubilei: Clustering Brain Connectomes Through a Density-peak Approach -- T. Górecki, M. ?uczak, and P. Piasecki: Similarity Forest for Time Series Classification -- K. Hayashi, E. Hoshino, M. Suzuki, E. Nakanishi, K. Sakai, and M. Obatake: Detection of the Biliary Atresia Using Deep Convolutional Neural Networks Based on Statistical Learning Weights via Optimal Similarity and Resampling Methods -- Ch. Hennig: Some Issues in Robust Clustering -- J. Kalina and P. Janá£ek: Robustness Aspects of Optimized Centroids -- L. Labiod and M. Nadif: Data Clustering and Representation Learning Based on Networked Data -- Lazhar Labiod and Mohamed Nadif: Towards a Bi-stochastic Matrix Approximation of k-means and Some Variants -- A. LaLonde, T. Love, D. R. Young, and T. Wu: Clustering Adolescent Female Physical Activity Levels with an Infinite Mixture Model on Random Effects -- Á. López-Oriona, J. A. Vilar, and P. D?Urso: Unsupervised Classification of Categorical Time Series Through Innovative Distances -- D. Masís, E. Segura, J. Trejos, and A. Xavier: Fuzzy Clustering by Hyperbolic Smoothing -- R. Meng, H. K. H. Lee, and K. Bouchard: Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networks -- H. Duy Nguyen, F. Forbes, G. Fort, and O. Cappé: An Online Minorization-Maximization Algorithm -- L. Palazzo and R. Ievoli: Detecting Differences in Italian Regional Health Services During Two Covid-19 Waves -- G. Panagiotidou and T. Chadjipadelis: Political and Religion Attitudes in Greece: Behavioral Discourses -- K. Pawlasová, I. Karafiátová, and J. Dvo?ák: Supervised Classification via Neural Networks for Replicated Point Patterns -- G. Perrone and G. Soffritti: Parsimonious Mixtures of Seemingly Unrelated Contaminated Normal Regression Models -- N. Pronello, R. Ignaccolo, L. Ippoliti, and S. Fontanella: Penalized Model-based Functional Clustering: a Regularization Approach via Shrinkage Methods -- D. Rodrigues, L. P. Reis, and B. M. Faria: Emotion Classification Based on Single Electrode Brain Data: Applications for Assistive Technology -- R. Scimone, A. Menafoglio, L. M. Sangalli, and P. Secchi: The Death Process in Italy Before and During the Covid-19 Pandemic: a Functional Compositional Approach -- O. Silva, Á. Sousa, and H. Bacelar-Nicolau: Clustering Validation in the Context of Hierarchical Cluster Analysis: an Empirical Study -- C. Silvestre, M. G. M. S. Cardoso, and M. Figueiredo: An MML Embedded Approach for Estimating the Number of Clusters -- Á. Sousa, O. Silva, M. Graça Batista, S. Cabral, and H. Bacelar-Nicolau: Typology of Motivation Factors for Employees in the Banking Sector: An Empirical Study Using Multivariate Data Analysis Methods -- J. Michael Spoor, J. Weber, and J. Ovtcharova: A Proposal for Formalization and Definition of Anomalies in Dynamical Systems -- N. Tahiri and A. Koshkarov: New Metrics for Classifying Phylogenetic Trees Using -means and the Symmetric Difference Metric -- S. D. Tomarchio: On Parsimonious Modelling via Matrix-variate t Mixtures -- G. Zammarchi, M. Romano, and C. Conversano: Evolution of Media Coverage on Climate Change and Environmental Awareness: an Analysisof Tweets from UK and US Newspapers. 330 $aThe contributions gathered in this open access book focus on modern methods for data science and classification and present a series of real-world applications. Numerous research topics are covered, ranging from statistical inference and modeling to clustering and dimension reduction, from functional data analysis to time series analysis, and network analysis. The applications reflect new analyses in a variety of fields, including medicine, marketing, genetics, engineering, and education. The book comprises selected and peer-reviewed papers presented at the 17th Conference of the International Federation of Classification Societies (IFCS 2022), held in Porto, Portugal, July 19?23, 2022. The IFCS federates the classification societies and the IFCS biennial conference brings together researchers and stakeholders in the areas of Data Science, Classification, and Machine Learning. It provides a forum for presenting high-quality theoretical and applied works, and promoting and fostering interdisciplinary research and international cooperation. The intended audience is researchers and practitioners who seek the latest developments and applications in the field of data science and classification. 410 0$aStudies in Classification, Data Analysis, and Knowledge Organization,$x2198-3321 606 $aArtificial intelligence$xData processing 606 $aMachine learning 606 $aData mining 606 $aMultivariate analysis 606 $aStatistics$xComputer programs 606 $aData Science 606 $aStatistical Learning 606 $aMachine Learning 606 $aData Mining and Knowledge Discovery 606 $aMultivariate Analysis 606 $aStatistical Software 606 $aIntel·ligència artificial$2thub 606 $aAprenentatge automàtic$2thub 606 $aMineria de dades$2thub 606 $aAnàlisi multivariable$2thub 608 $aLlibres electrònics$2thub 615 0$aArtificial intelligence$xData processing. 615 0$aMachine learning. 615 0$aData mining. 615 0$aMultivariate analysis. 615 0$aStatistics$xComputer programs. 615 14$aData Science. 615 24$aStatistical Learning. 615 24$aMachine Learning. 615 24$aData Mining and Knowledge Discovery. 615 24$aMultivariate Analysis. 615 24$aStatistical Software. 615 7$aIntel·ligència artificial 615 7$aAprenentatge automàtic 615 7$aMineria de dades 615 7$aAnàlisi multivariable 676 $a005.7 700 $aBrito$b Paula$01459040 701 $aDias$b José G$01459041 701 $aLausen$b Berthold$01459042 701 $aMontanari$b Angela$0100774 701 $aNugent$b Rebecca$01459043 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK9 912 $a9910768481303321 996 $aClassification and Data Science in the Digital Age$93658465 997 $aUNINA