Titlebook: Effective Statistical Learning Methods for Actuaries I; GLMs and Extensions Michel Denuit,Donatien Hainaut,Julien Trufin Textbook 2019 Spri

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,Das Reichselektrizit?tsmonopol,y results in correlation among the responses within the same group, casting doubts about the outputs of analyses assuming mutual independence. Random effects offer a convenient way to model such grouping structure. This chapter presents the Generalized Linear Mixed Model (GLMM) approach to regressio

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,Die Landes-Versicherungs?mter,eatures coded by means of binary variables. However, this assumption becomes questionable for continuous features which may have a nonlinear effect on the score scale. This chapter is devoted to Generalized Additive Models (GAMs) which keep the additive decomposition of the score but allow the actua

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https://doi.org/10.1007/978-3-642-94474-1ion, scale, shape or probability mass at the origin, for instance. This allows the actuary to let the available information enter other dimensions of the response, such as volatility or no-claim probability. The double GLM setting supplements GLMs with dispersion modeling, letting the dispersion par

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Effective Statistical Learning Methods for Actuaries I978-3-030-25820-7Series ISSN 2523-3262 Series E-ISSN 2523-3270