对WAIC和后续预测方法进行N混合模型的比较
Heather E Gaya1, Alison C Ketz2
1Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, 30602, USA. heather.e.gaya@gmail.com.
Scientific reports
|July 8, 2024
概括
对广泛适用信息标准 (WAICj) 的新联合概率方法对于选择生态N混合物模型更为准确. 这种强大的方法超过了标准WAIC和后预测损失,即使有时间相关性.
科学领域:
- 生态建模 生态建模
- 统计生态学 统计生态学
- 野生动物种群分析
背景情况:
- 层次模型在生态分析中被广泛使用.
- 选择适合生态数据的模型,特别是N混合模型,是一个重大挑战.
- 现有的模型选择标准在各种生态数据结构下可能不会发挥最佳作用.
研究的目的:
- 评估和比较N混合模型的不同模型选择标准的性能.
- 确定在生态研究中选择候选N混合物模型中最准确的方法.
- 在时间自相关性下评估模型选择标准的稳定性.
主要方法:
- 条件广泛适用信息标准 (WAIC),联合概率 WAIC (WAICj) 和后期预测损失的比较.
- 在模拟的单季N混合模型上测试模型选择标准.
- 对模拟的多季度N混合模型的评估标准,具有时间自相关性.
- 应用到使用eBird数据对单季N混合模型的三个案例研究.
主要成果:
- 与条件WAIC相比,WAIC (WAICj) 的联合概率方法显示出更高的准确性.
- 在模型选择准确度方面,WAICj的表现优于后预测损失.
- WAICj的增强准确性即使在模型包含时间自相关性时也是一致的.
- 在模拟和现实世界的生态数据集中评估了性能.
结论:
- WAICj是生态学中N混合物模型的更准确和更强大的模型选择标准.
- 这种联合概率方法为生态模型选择提供了可靠的替代方案,特别是在存在时间依赖的情况下.
- 这些发现为生态学家在选择适合人口分析的统计模型方面提供了指导.
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