Linear Mixed Models for Predictive Modelling in Actuarial Science

Chapter 8 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 266-312.

36 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 28, 2013

Abstract

We give a general discussion of linear mixed models and continue with illustrating specific actuarial applications of this type of models. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques and methods of inference. We include three worked out examples with the R lme4 package and use ggplot2 for the graphs. Full code is available from the book project's web page.

Suggested Citation

Antonio, Katrien and Zhang, Yanwei, Linear Mixed Models for Predictive Modelling in Actuarial Science (November 28, 2013). Chapter 8 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 266-312.. Available at SSRN: https://ssrn.com/abstract=2381505 or http://dx.doi.org/10.2139/ssrn.2381505

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

Register to save articles to
your library

Register

Paper statistics

Downloads
80
rank
299,457
Abstract Views
360
PlumX Metrics