Effective Statistical Learning Methods for Actuaries I GLMs and Extensions /

• Κύριοι συγγραφείς: Denuit, Michel (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Hainaut, Donatien (http://id.loc.gov/vocabulary/relators/aut), Trufin, Julien (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Springer Actuarial Lecture Notes,
• Θέματα: Actuarial science.; Statistics .; Actuarial Sciences.; Statistics for Business, Management, Economics, Finance, Insurance.

This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling,...

Πίνακας περιεχομένων:

• Preface
• Part I: LOSS MODELS.-1. Insurance Risk Classification.-Exponential Dispersion (ED) Distributions.-3.-Maximum Likelihood Estimation.-Part II LINEAR MODELS.-4. Generalized Linear Models (GLMs)
• 5.-Over-dispersion, credibility adjustments, mixed models, and regularization.-Part III ADDITIVE MODELS
• 6 Generalized Additive Models (GAMs)
• 7. Beyond Mean Modeling: Double GLMs and GAMs for Location, Scale and Shape (GAMLSS)
• Part IV SPECIAL TOPICS
• 8. Some Generalized Non-Linear Models (GNMs)
• 9 Extreme Value Models
• References.