On the undecidability among kinetic models: From model selection to model averaging
On the undecidability among kinetic models: From model selection to model averaging
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DOI：
10.1097/01.wcb.0000050065.57184.bb
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发表时间：
2003-04-01
影响因子：
6.3
通讯作者：
Cunningham, VJ
中科院分区：
文献类型：
--
作者：
Turkheimer, FE;Hinz, R;Cunningham, VJ
本文讨论了核医学中示踪剂动力学数学描述的模型选择问题。它源于对一些特定数据集的考虑，其中不同的模型具有相似的性能。在这些情况下，它表明，考虑平均的参数的估计在整个模型集是优于仅从一个模型获得的估计。此外，它还表明，在一个小数目的“好”的模型的平均过程减少了“泛化误差”，引入的错误时，选择一个特定的数据集的模型被应用到不同的条件下，如受试者群体与改变生理参数，修改后的采集协议，和不同的信噪比。对整个模型集求平均值的方法使用赤池系数作为单个模型可能性的度量。为了便于理解这些统计工具，作者介绍了模型选择标准和赤池的信息理论方法的简短技术处理。新的方法是说明和概括的情况下，[C-11]氟马西尼动力学在大脑中，包含真实的和模拟数据建模的例子。
This article deals with the problem of model selection for the mathematical description of tracer kinetics in nuclear medicine. It stems from the consideration of some specific data sets where different models have similar performances. In these situations, it is shown that considerate averaging of a parameter's estimates over the entire model set is better than obtaining the estimates from one model only. Furthermore, it is also shown that the procedure of averaging over a small number of "good" models reduces the "generalization error," the error introduced when the model selected over a particular data set is applied to different conditions, such as subject populations with altered physiologic parameters, modified acquisition Protocols, and different signal-to-noise ratios. The method of averaging over the entire model set uses Akaike coefficients as measures of an individual model's likelihood. To facilitate the understanding of these statistical tools, the authors provide an introduction to model selection criteria and a short technical treatment of Akaike's information-theoretic approach. The new method is illustrated and epitomized by a case example on the modeling of [C-11]flumazenil kinetics in the brain, containing both real and simulated data.