期刊
BIOMETRICS
卷 68, 期 3, 页码 878-885出版社
WILEY-BLACKWELL
DOI: 10.1111/j.1541-0420.2011.01729.x
关键词
Abundance; Bayesian modeling; Link function; Log score loss; Poisson-Gamma; Presence; absence
资金
- NSF-CDI [0940671]
- Direct For Biological Sciences
- Emerging Frontiers [1137309, 1137364] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [0940671] Funding Source: National Science Foundation
Many applications involve count data from a process that yields an excess number of zeros. Zero-inflated count models, in particular, zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models, along with Poisson hurdle models, are commonly used to address this problem. However, these models struggle to explain extreme incidence of zeros (say more than 80%), especially to find important covariates. In fact, the ZIP may struggle even when the proportion is not extreme. To redress this problem we propose the class of k-ZIG models. These models allow more flexible modeling of both the zero-inflation and the nonzero counts, allowing interplay between these two components. We develop the properties of this new class of models, including reparameterization to a natural link function. The models are straightforwardly fitted within a Bayesian framework. The methodology is illustrated with simulated data examples as well as a forest seedling dataset obtained from the USDA Forest Services Forest Inventory and Analysis program.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据