AIC under the framework of least squares estimation
AIC under the framework of least squares estimation
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DOI：
10.1016/j.aml.2017.05.005
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发表时间：
2017-12-01
影响因子：
3.7
通讯作者：
Joyner, Michele L.
中科院分区：
文献类型：
--
作者：
Banks, H. T.;Joyner, Michele L.
在本笔记中，我们解释了Akiake信息准则及其相关模型比较指数(通常用于最大似然估计器反问题公式)在最小二乘(普通、加权、迭代加权或“广义”等)的背景下的使用。基于反问题的公式。用几个对生物学感兴趣的例子来说明这些想法。(C)爱思唯尔有限公司发布的2017年。
In this note we explain the use of the Akiake Information Criterion and its related model comparison indices (usually derived for maximum likelihood estimator inverse problem formulations) in the context of least squares (ordinary, weighted, iterative weighted or "generalized", etc.) based inverse problem formulations. The ideas are illustrated with several examples of interest in biology. (C) 2017 Published by Elsevier Ltd.