Non Linear Mixed Models for Predictive Modelling in Actuarial Science

Chapter 16 in Predictive Modeling Applications in Actuarial Science, Vol. 1 Predictive Modeling Techniques. (Editors E.W. Frees, R.A. Derrig, G. G. Meyers). Cambridge University Press, pages 566-601. (With peer review)

27 Pages Posted: 20 Jan 2014 Last revised: 17 May 2017

See all articles by Katrien Antonio

Katrien Antonio

KU Leuven; University of Amsterdam

Yanwei Zhang

University of Chicago - Department of Statistics

Date Written: November 24, 2013

Abstract

We start with a discussion of model families for multilevel data outside the Gaussian framework. We continue with Generalized Linear Mixed Models ([GLMMs]), which enable generalized linear modeling with multilevel data. The Chapter includes highlights of estimation techniques for GLMMs, in the frequentist as well as Bayesian context. We continue with a discussion of Non Linear Mixed Models ([NLMMs]). The Chapter concludes with an extensive case study using a selection of R packages for GLMMs.

Suggested Citation

Antonio, Katrien and Zhang, Yanwei, Non Linear Mixed Models for Predictive Modelling in Actuarial Science (November 24, 2013). Chapter 16 in Predictive Modeling Applications in Actuarial Science, Vol. 1 Predictive Modeling Techniques. (Editors E.W. Frees, R.A. Derrig, G. G. Meyers). Cambridge University Press, pages 566-601. (With peer review). Available at SSRN: https://ssrn.com/abstract=2381492 or http://dx.doi.org/10.2139/ssrn.2381492

University of Amsterdam ( email )

Roetersstraat 11
Amsterdam, 1018 WB
Netherlands

Yanwei Zhang

University of Chicago - Department of Statistics ( email )

Eckhart Hall Room 108
5734 S. University Avenue
Chicago, IL 60637
United States

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