Tweedie’s Compound Poisson Model With Grouped Elastic Net

Wei Qian, Yi Yang, Hui Zou

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Tweedie’s compound Poisson model is a popular method to model data with probability mass at zero and nonnegative, highly right-skewed distribution. Motivated by wide applications of the Tweedie model in various fields such as actuarial science, we investigate the grouped elastic net method for the Tweedie model in the context of the generalized linear model. To efficiently compute the estimation coefficients, we devise a two-layer algorithm that embeds the blockwise majorization descent method into an iteratively reweighted least square strategy. Integrated with the strong rule, the proposed algorithm is implemented in an easy-to-use R package HDtweedie, and is shown to compute the whole solution path very efficiently. Simulations are conducted to study the variable selection and model fitting performance of various lasso methods for the Tweedie model. The modeling applications in risk segmentation of insurance business are illustrated by analysis of an auto insurance claim dataset. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)606-625
Number of pages20
JournalJournal of Computational and Graphical Statistics
Volume25
Issue number2
DOIs
StatePublished - Apr 2 2016

Bibliographical note

Publisher Copyright:
© 2016 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Keywords

  • Coordinate descent
  • IRLS-BMD
  • Insurance score
  • Lasso
  • Variable sele-ction

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