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Effective Statistical Learning Methods for Actuaries I: GLMs and Extensions /

Effective Statistical Learning Methods for Actuaries I: GLMs and Extensions /
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Field name Details
Dewey Class 368.01
Title Effective Statistical Learning Methods for Actuaries I ([EBook] :) : GLMs and Extensions / / by Michel Denuit, Donatien Hainaut, Julien Trufin.
Author Denuit, Michel
Added Personal Name Hainaut, Donatien
Trufin, Julien
Other name(s) SpringerLink (Online service)
Edition statement 1st ed. 2019.
Publication Cham : : Springer International Publishing : : Imprint: Springer, , 2019.
Physical Details XVI, 441 p. 82 illus., 23 illus. in color. : online resource.
ISBN 9783030258207
Summary Note 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, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.:
Contents note 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.
System details note Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site https://doi.org/10.1007/978-3-030-25820-7
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