Dynamic spatio-temporal zero-inflated Poisson models for predicting capelin distribution in the Barents Sea
Dynamic spatio-temporal zero-inflated Poisson models for predicting capelin distribution in the Barents Sea
复制标题
DOI：
10.1007/s42081-022-00183-x
复制
发表时间：
2021-11
影响因子：
1.3
通讯作者：
S. Sugasawa;Tomoyuki Nakagawa;H. Solvang;S. Subbey;Salah Alrabeei
中科院分区：
文献类型：
--
作者：
S. Sugasawa;Tomoyuki Nakagawa;H. Solvang;S. Subbey;Salah Alrabeei
我们考虑建模和预测的毛鳞鱼分布在巴伦支海的基础上，在指定的调查区域连续变化的零膨胀计数观测数据。该模型是一个混合的两个组件，一个在原点的一点分布和泊松分布的时空强度，其中强度和混合比例建模的一些辅助变量和未观察到的时空效应。时空效应的建模与预测高斯过程相结合的动态线性模型。我们开发了一个有效的后验计算算法的模型，使用数据增强策略。该模型的性能通过模拟研究得到了证明，并应用于2014年至2019年在巴伦支海捕获的毛鳞鱼数量。
We consider modeling and prediction of Capelin distribution in the Barents Sea based on zero-inflated count observation data that vary continuously over a specified survey region. The model is a mixture of two components; a one-point distribution at the origin and a Poisson distribution with spatio-temporal intensity, where both intensity and mixing proportions are modeled by some auxiliary variables and unobserved spatio-temporal effects. The spatio-temporal effects are modeled by a dynamic linear model combined with the predictive Gaussian process. We develop an efficient posterior computational algorithm for the model using a data augmentation strategy. The performance of the proposed model is demonstrated through simulation studies, and an application to the number of Capelin caught in the Barents Sea from 2014 to 2019.