Over-Dispersed Age-Period-Cohort Models

• 成果类型: Article
• 署名作者: Harnau, Jonas; Nielsen, Bent
• 署名单位: University of Oxford
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1366908
• 发表日期: 2018
• 页码: 1722-1732
• 关键词: chain-ladder; inference

摘要

We consider inference and forecasting for aggregate data organized in a two-way table with age and cohort as indices, but without measures of exposure. This is modeled using a Poisson likelihood with an age-period-cohort structure for the mean while allowing for over-dispersion. We propose a repetitive structure that keeps the dimension of the table fixed while increasing the latent exposure. For this, we use a class of infinitely divisible distributions which include a variety of compound Poisson models and Poisson mixture models. This results in asymptotic F inference and t forecast distributions.