Parameter redundancy in discrete state-space and integrated models.

Parameter redundancy in discrete state-space and integrated models.
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DOI:
10.1002/bimj.201400239
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发表时间:
2016-09
影响因子:
1.7
通讯作者:
McCrea, Rachel S.
McCrea, Rachel S.
中科院分区:
生物学3区
文献类型:
--
作者:
Cole, Diana J.;McCrea, Rachel S.

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离散状态空间模型在生态学中用于描述野生动物种群的动态,其参数,如生存概率,具有生态学意义。对于模型的特定参数化,并不总是清楚可以估计哪些参数。这种无法估计所有参数的情况称为参数冗余,或者模型被描述为不可识别。在本文中,我们开发了可用于检测离散状态空间模型中参数冗余的方法。详尽摘要是完全指定模型的参数组合。为了使用检测参数冗余的一般方法,需要适当的详尽总结。本文提出了两种方法,用于使用连续状态空间模型方法的离散类似物推导离散状态空间模型的详尽摘要。我们还表明,通过使用一个综合的人口模型,结合多个数据集,可能会导致在一个模型中,所有的参数是可估计的,即使模型拟合到单独的数据集可能是参数冗余。
Discrete state‐space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state‐space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state‐space models using discrete analogues of methods for continuous state‐space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant.
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