A one‐step‐ahead pseudo‐DIC for comparison of Bayesian state‐space models
A one‐step‐ahead pseudo‐DIC for comparison of Bayesian state‐space models
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DOI：
10.1111/biom.12237
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发表时间：
2014-12
期刊：
影响因子：
1.9
通讯作者：
Russell B. Millar;S. Mckechnie
中科院分区：
文献类型：
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作者：
Russell B. Millar;S. Mckechnie
在状态空间建模的背景下，通常使用偏差信息准则（DIC）来评估模型在给定时间t的潜在状态的情况下预测时间t的观测结果的能力。由于传统DIC无法在竞争的多变量非线性贝叶斯状态空间模型之间进行清晰选择，以及替代方案的计算挑战，本研究提出了一种超前一步的DIC， DICp，其中预测以前一个时间点的状态为条件。模拟结果表明，DICp可以很好地选择具有不同过程或观测方程的状态空间模型。相比之下，传统的DIC可能具有严重的误导性，对错误模型有着强烈的偏好。这可以解释为它未能解释由模型不规范引起的过程误差的夸大估计。DICp不是基于一个真正的条件似然，但被证明可以解释为一个伪DIC，其中消除了膨胀过程误差的补偿行为。当过程方程和观测方程为共轭时，可以使用流行的BUGS软件中的DIC监测器轻松计算。DICp的改进性能通过应用于俄勒冈州龙虾溪鳕鱼丰度的多阶段建模得到了证明。
In the context of state‐space modeling, conventional usage of the deviance information criterion (DIC) evaluates the ability of the model to predict an observation at time t given the underlying state at time t. Motivated by the failure of conventional DIC to clearly choose between competing multivariate nonlinear Bayesian state‐space models for coho salmon population dynamics, and the computational challenge of alternatives, this work proposes a one‐step‐ahead DIC, DICp , where prediction is conditional on the state at the previous time point. Simulations revealed that DICp worked well for choosing between state‐space models with different process or observation equations. In contrast, conventional DIC could be grossly misleading, with a strong preference for the wrong model. This can be explained by its failure to account for inflated estimates of process error arising from the model mis‐specification. DICp is not based on a true conditional likelihood, but is shown to have interpretation as a pseudo‐DIC in which the compensatory behavior of the inflated process errors is eliminated. It can be easily calculated using the DIC monitors within popular BUGS software when the process and observation equations are conjugate. The improved performance of DICp is demonstrated by application to the multi‐stage modeling of coho salmon abundance in Lobster Creek, Oregon.