Journal article
A Tweedie Compound Poisson Model in Reproducing Kernel Hilbert Space
Technometrics, Vol.65(2), pp.281-295
2023
DOI: 10.1080/00401706.2022.2156615
Abstract
Tweedie models can be used to analyze nonnegative continuous data with a probability mass at zero. There have been wide applications in natural science, healthcare research, actuarial science, and other fields. The performance of existing Tweedie models can be limited on today’s complex data problems with challenging characteristics such as nonlinear effects, high-order interactions, high-dimensionality and sparsity. In this article, we propose a kernel Tweedie model, Ktweedie, and its sparse variant, SKtweedie, that can simultaneously address the above challenges. Specifically, nonlinear effects and high-order interactions can be flexibly represented through a wide range of kernel functions, which is fully learned from the data; In addition, while the Ktweedie can handle high-dimensional data, the SKtweedie with integrated variable selection can further improve the interpretability. We perform extensive simulation studies to justify the prediction and variable selection accuracy of our method, and demonstrate the applications in ratemaking and loss-reserving in general insurance. Overall, the Ktweedie and SKtweedie outperform existing Tweedie models when there exist nonlinear effects and high-order interactions, particularly when the dimensionality is high relative to the sample size. The model is implemented in an efficient and user-friendly R package ktweedie (https://cran.r-project.org/package=ktweedie).
Details
- Title: Subtitle
- A Tweedie Compound Poisson Model in Reproducing Kernel Hilbert Space
- Creators
- Yi Lian - McGill UniversityArcher Yi Yang - McGill UniversityBoxiang Wang - University of IowaPeng Shi - University of Wisconsin–MadisonRobert William Platt - McGill University
- Resource Type
- Journal article
- Publication Details
- Technometrics, Vol.65(2), pp.281-295
- DOI
- 10.1080/00401706.2022.2156615
- ISSN
- 0040-1706
- eISSN
- 1537-2723
- Grant note
- DOI: 10.13039/501100000038, name: Natural Sciences and Engineering Research Council of Canada, award: PGSD2-519554-2018; DOI: 10.13039/501100000038, name: NSERC, award: RGPIN-2016-05174
- Language
- English
- Electronic publication date
- 12/13/2022
- Date published
- 2023
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984339543502771
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