01218cam2 22002891 450 SOBE0003932120140210073819.020140122d1929 |||||ita|0103 bafrelatFR<<6:>>Seconde action contre Verrès. Livre 5.: Les supplicesCicérontexte établi par Henri Bornecque et traduit par Gaston RabaudParisLes belles lettres1929XV, 99 p. (4-99 doppie)20 cmTesto originale a fronte001SOBE000392352001 Discours / CicéronCicero, Marcus TulliusAF0001343507082411Bornecque, HenriSOBA00008506070Rabaud, GastonSOBA00009179070ITUNISOB20140210RICAUNISOBUNISOB3|C11412|ortSOBE00039321M 102 Monografia moderna SBNMFondo|Ortolani3|C031-3.6modalità di consultazione sulla home page della Biblioteca link FondiNO11412|ortOrtolaniSdonocalvano123UNISOBUNISOB20140122080157.020140122080616.0calvano123Seconde action contre Verrès. Livre 5.: Les supplices1713172UNISOB05425nam 2200661 a 450 991102008380332120200520144314.09786610974061978128097406912809740609780470517420047051742597804705174130470517417(CKB)1000000000357139(EBL)315071(SSID)ssj0000096988(PQKBManifestationID)11126391(PQKBTitleCode)TC0000096988(PQKBWorkID)10113627(PQKB)10412079(MiAaPQ)EBC315071(OCoLC)184983241(Perlego)2750279(EXLCZ)99100000000035713920070517d2007 uy 0engur|n|---|||||txtccrActuarial modelling of claim counts risk classification, credibility and bonus-malus systems /Michel Denuit ... [et al.]Chichester, West Sussex, England ;Hoboken, NJ Wileyc20071 online resource (386 p.)Description based upon print version of record.9780470026779 0470026774 Includes bibliographical references (p. [345]-353) and index.Actuarial Modelling of Claim Counts; Contents; Foreword; Preface; Notation; Part I Modelling Claim Counts; 1 Mixed Poisson Models for Claim Numbers; 1.1 Introduction; 1.1.1 Poisson Modelling for the Number of Claims; 1.1.2 Heterogeneity and Mixed Poisson Model; 1.1.3 Maximum Likelihood Estimation; 1.1.4 Agenda; 1.2 Probabilistic Tools; 1.2.1 Experiment and Universe; 1.2.2 Random Events; 1.2.3 Sigma-Algebra; 1.2.4 Probability Measure; 1.2.5 Independent Events; 1.2.6 Conditional Probability; 1.2.7 Random Variables and Random Vectors; 1.2.8 Distribution Functions1.2.9 Independence for Random Variables1.3 Poisson Distribution; 1.3.1 Counting Random Variables; 1.3.2 Probability Mass Function; 1.3.3 Moments; 1.3.4 Probability Generating Function; 1.3.5 Convolution Product; 1.3.6 From the Binomial to the Poisson Distribution; 1.3.7 Poisson Process; 1.4 Mixed Poisson Distributions; 1.4.1 Expectations of General Random Variables; 1.4.2 Heterogeneity and Mixture Models; 1.4.3 Mixed Poisson Process; 1.4.4 Properties of Mixed Poisson Distributions; 1.4.5 Negative Binomial Distribution; 1.4.6 Poisson-Inverse Gaussian Distribution1.4.7 Poisson-LogNormal Distribution1.5 Statistical Inference for Discrete Distributions; 1.5.1 Maximum Likelihood Estimators; 1.5.2 Properties of the Maximum Likelihood Estimators; 1.5.3 Computing the Maximum Likelihood Estimators with the Newton-Raphson Algorithm; 1.5.4 Hypothesis Tests; 1.6 Numerical Illustration; 1.7 Further Reading and Bibliographic Notes; 1.7.1 Mixed Poisson Distributions; 1.7.2 Survey of Empirical Studies Devoted to Claim Frequencies; 1.7.3 Semiparametric Approach; 2 Risk Classification; 2.1 Introduction; 2.1.1 Risk Classification, Regression Models and Random Effects2.1.2 Risk Sharing in Segmented Tariffs2.1.3 Bonus Hunger and Censoring; 2.1.4 Agenda; 2.2 Descriptive Statistics for Portfolio A; 2.2.1 Global Figures; 2.2.2 Available Information; 2.2.3 Exposure-to-Risk; 2.2.4 One-Way Analyses; 2.2.5 Interactions; 2.2.6 True Versus Apparent Dependence; 2.3 Poisson Regression Model; 2.3.1 Coding Explanatory Variables; 2.3.2 Loglinear Poisson Regression Model; 2.3.3 Score; 2.3.4 Multiplicative Tariff; 2.3.5 Likelihood Equations; 2.3.6 Interpretation of the Likelihood Equations; 2.3.7 Solving the Likelihood Equations with the Newton-Raphson Algorithm2.3.8 Wald Confidence Intervals2.3.9 Testing for Hypothesis on a Single Parameter; 2.3.10 Confidence Interval for the Expected Annual Claim Frequency; 2.3.11 Deviance; 2.3.12 Deviance Residuals; 2.3.13 Testing a Hypothesis on a Set of Parameters; 2.3.14 Specification Error and Robust Inference; 2.3.15 Numerical Illustration; 2.4 Overdispersion; 2.4.1 Explanation of the Phenomenon; 2.4.2 Interpreting Overdispersion; 2.4.3 Consequences of Overdispersion; 2.4.4 Modelling Overdispersion; 2.4.5 Detecting Overdispersion; 2.4.6 Testing for Overdispersion; 2.5 Negative Binomial Regression Model2.5.1 Likelihood EquationsThere are a wide range of variables for actuaries to consider when calculating a motorist's insurance premium, such as age, gender and type of vehicle. Further to these factors, motorists' rates are subject to experience rating systems, including credibility mechanisms and Bonus Malus systems (BMSs). Actuarial Modelling of Claim Counts presents a comprehensive treatment of the various experience rating systems and their relationships with risk classification. The authors summarize the most recent developments in the field, presenting ratemaking systems, whilst taking into account eAutomobile insuranceRatesEuropeAutomobile insurance claimsEuropeAutomobile insuranceRatesAutomobile insurance claims368/.092094Denuit M(Michel)781288MiAaPQMiAaPQMiAaPQBOOK9911020083803321Actuarial modelling of claim counts4417700UNINA