LEADER 05638nam 2200649 a 450 001 9910451940403321 005 20210526214011.0 010 $a1-283-39655-6 010 $a9786613396556 010 $a3-11-019754-5 024 7 $a10.1515/9783110197549 035 $a(CKB)1000000000479969 035 $a(EBL)322938 035 $a(OCoLC)476120325 035 $a(SSID)ssj0000192664 035 $a(PQKBManifestationID)11190062 035 $a(PQKBTitleCode)TC0000192664 035 $a(PQKBWorkID)10218089 035 $a(PQKB)11311805 035 $a(MiAaPQ)EBC322938 035 $a(DE-B1597)32225 035 $a(OCoLC)853236860 035 $a(OCoLC)948655808 035 $a(DE-B1597)9783110197549 035 $a(Au-PeEL)EBL322938 035 $a(CaPaEBR)ebr10197179 035 $a(CaONFJC)MIL339655 035 $a(EXLCZ)991000000000479969 100 $a20051102d2005 uy 0 101 0 $aeng 135 $aurun#---|u||u 181 $ctxt 182 $cc 183 $acr 200 00$aLinguistic evidence$b[electronic resource] $eempirical, theoretical, and computational perspectives /$fedited by Stefan Kepser, Marga Reis 210 $aBerlin ;$aNew York $cMouton de Gruyter$dc2005 215 $a1 online resource (592 p.) 225 1 $aStudies in generative grammar ;$v85 300 $aDescription based upon print version of record. 311 0 $a3-11-018312-9 320 $aIncludes bibliographical references. 327 $tFront matter --$tContents --$tEvidence in Linguistics --$tGradedness and Consistency in Grammaticality Judgments --$tNull Subjects and Verb Placement in Old High German --$tBeauty and the Beast: What Running a Broad-Coverage Precision Grammar over the BNC Taught Us about the Grammar - and the Corpus --$tSeemingly Indefinite Definites --$tAnimacy as a Driving Cue in Change and Acquisition in Brazilian Portuguese --$tAspectual Coercion and On-line Processing: The Case of Iteration --$tWhy Do Children Fail to Understand Weak Epistemic Terms? An Experimental Study --$tProcessing Negative Polarity Items: When Negation Comes Through the Backdoor --$tLinguistic Constraints on the Acquisition of Epistemic Modal Verbs --$tThe Decathlon Model of Empirical Syntax --$tExamining the Constraints on the Benefactive Alternation by Using the World Wide Web as a Corpus --$tA Quantitative Corpus Study of German Word Order Variation --$tWhich Statistics Reflect Semantics? Rethinking Synonymy and Word Similarity --$tLanguage Production Errors as Evidence for Language Production Processes - The Frankfurt Corpora --$tA Multi-Evidence Study of European and Brazilian Portuguese wh-Questions --$tThe Relationship between Grammaticality Ratings and Corpus Frequencies: A Case Study into Word Order Variability in the Midfield of German Clauses --$tThe Emergence of Productive Non-Medical -itis: Corpus Evidence and Qualitative Analysis --$tExperimental Data vs. Diachronic Typological Data: Two Types of Evidence for Linguistic Relativity --$tReflexives and Pronouns in Picture Noun Phrases: Using Eye Movements as a Source of Linguistic Evidence --$tThe Plural is Semantically Unmarked --$tCoherence - an Experimental Approach --$tThinking About What We Are Asking Speakers to Do --$tA Prosodic Factor for the Decline of Topicalisation in English --$tOn the Syntax of DP Coordination: Combining Evidence from Reading-Time Studies and Agrammatic Comprehension --$tLexical Statistics and Lexical Processing: Semantic Density, Information Complexity, Sex, and Irregularity in Dutch --$tThe Double Competence Hypothesis On Diachronic Evidence --$tBack matter 330 $aThe renaissance of corpus linguistics and promising developments in experimental linguistic techniques in recent years have led to a remarkable revival of interest in issues of the empirical base of linguistic theory in general, and the status of different kinds of linguistic evidence in particular. Consensus is growing (a) that even so-called primary data (from introspection as well as authentic language production) are inherently complex performance data only indirectly reflecting the subject of linguistic theory, (b) that for an appropriate foundation of linguistic theories evidence from different sources such as introspective data, corpus data, data from (psycho-)linguistic experiments, historical and diachronic data, typological data, neurolinguistic data and language learning data are not only welcome but also often necessary. It is in particular by contrasting evidence from different sources with respect to particular research questions that we may gain a deeper understanding of the status and quality of the individual types of linguistic evidence on the one hand, and of their mutual relationship and respective weight on the other. The present volume is a collection of (selected) papers presented at the conference on 'Linguistic Evidence' in Tübingen 2004, which was explicitly devoted to the above issues. All of them address these issues in relation to specific linguistic research problems, thereby helping to establish a better understanding of the nature of linguistic evidence in particularly insightful ways. 410 0$aStudies in generative grammar ;$v85. 606 $aLinguistics$xMethodology 608 $aElectronic books. 615 0$aLinguistics$xMethodology. 676 $a410.72 701 $aKepser$b Stephan$f1967-$01027390 701 $aReis$b Marga$0317654 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910451940403321 996 $aLinguistic evidence$92442796 997 $aUNINA LEADER 05411nam 2200661 a 450 001 9910829828703321 005 20170815112944.0 010 $a1-119-20933-1 010 $a1-280-74003-5 010 $a9786610740031 010 $a0-470-06043-3 035 $a(CKB)1000000000357284 035 $a(EBL)284349 035 $a(OCoLC)123820265 035 $a(SSID)ssj0000238659 035 $a(PQKBManifestationID)12022623 035 $a(PQKBTitleCode)TC0000238659 035 $a(PQKBWorkID)10233158 035 $a(PQKB)10602018 035 $a(PQKBManifestationID)16114083 035 $a(PQKB)23737320 035 $a(MiAaPQ)EBC284349 035 $a(EXLCZ)991000000000357284 100 $a20061006d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRisk quantification$b[electronic resource] $emanagement, diagnosis and hedging /$fLaurent Condamin, Jean-Paul Louisot, Patrick Nai?m 210 $aChichester, West Sussex, England ;$aHoboken, NJ $cJohn Wiley$dc2006 215 $a1 online resource (287 p.) 225 1 $aWiley finance series 300 $aDescription based upon print version of record. 311 $a0-470-01907-7 320 $aIncludes bibliographical references and index. 327 $aRisk Quantification; Contents; Foreword; Introduction; 1 Foundations; Risk management: Principles and Practice; Definitions; Systematic and Unsystematic Risk; Insurable Risks; Exposure; Management; Risk Management; Risk Management Objectives; Organizational Objectives; Other Significant Objectives; Risk Management Decision Process; Step 1-Diagnosis of Exposures; Step 2-Risk Treatment; Step 3-Audit and Corrective Actions; State of the Art and the Trends in risk Management; Risk Profile, Risk Map or Risk Matrix; Frequency x Severity; Risk Financing and Strategic Financing 327 $aFrom Risk Management to Strategic Risk ManagementFrom Managing Physical Assets to Managing Reputation; From Risk Manager to Chief Risk Officer; Why is Risk Quantification Needed?; Risk Quantification - A Knowledge-Based Approach; Introduction; Causal Structure of Risk; Building a Quantitative Causal Model of Risk; Exposure, Frequency, and Probability; Exposure, Occurrence, and Impact Drivers; Controlling Exposure, Occurrence, and Impact; Controllable, Predictable, Observable, and Hidden Drivers; Cost of Decisions; Risk Financing; Risk Management Programme as an Influence Diagram 327 $aModelling an Individual Risk or the Risk Management ProgrammeSummary; 2 Tool Box; Probability Basics; Introduction to Probability Theory; Conditional Probabilities; Independence; Bayes' Theorem; Random Variables; Moments of a Random Variable; Continuous Random Variables; Main Probability Distributions; Introduction-the Binomial Distribution; Overview of Usual Distributions; Fundamental Theorems of Probability Theory; Empirical Estimation; Estimating Probabilities from Data; Fitting a Distribution from Data; Expert Estimation; From Data to Knowledge 327 $aEstimating Probabilities from Expert KnowledgeEstimating a Distribution from Expert Knowledge; Identifying the Causal Structure of a Domain; Conclusion; Bayesian Networks and Influence Diagrams; Introduction to the Case; Introduction to Bayesian Networks; Nodes and Variables; Probabilities; Dependencies; Inference; Learning; Extension to Influence Diagrams; Introduction to Monte Carlo Simulation; Introduction; Introductory Example: Structured Funds; Risk Management Example 1 - Hedging Weather Risk; Description; Collecting Information; Model; Manual Scenario; Monte Carlo Simulation; Summary 327 $aRisk Management Example 2- Potential Earthquake in Cement IndustryAnalysis; Model; Monte Carlo Simulation; Conclusion; A Bit of Theory; Introduction; Definition; Estimation According to Monte Carlo Simulation; Random Variable Generation; Variance Reduction; Software Tools; 3 Quantitative Risk Assessment: A Knowledge Modelling Process; Introduction; Increasing Awareness of Exposures and Stakes; Objectives of Risk Assessment; Issues in Risk Quantification; Risk Quantification: A Knowledge Management Process; The Basel II Framework for Operational Risk; Introduction; The Three Pillars 327 $aOperational Risk 330 $aThis book offers a practical answer for the non-mathematician to all the questions any businessman always wanted to ask about risk quantification, and never dare to ask. Enterprise-wide risk management (ERM) is a key issue for board of directors worldwide. Its proper implementation ensures transparent governance with all stakeholders' interests integrated into the strategic equation. Furthermore, Risk quantification is the cornerstone of effective risk management,at the strategic and tactical level, covering finance as well as ethics considerations. Both downside and upside ris 410 0$aWiley finance series. 606 $aRisk management$xMathematical models 615 0$aRisk management$xMathematical models. 676 $a332.6 676 $a658.15/5 700 $aCondamin$b Laurent$01643838 701 $aLouisot$b Jean-Paul$01643839 701 $aNai?m$b Patrick$0857114 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910829828703321 996 $aRisk quantification$93989332 997 $aUNINA