AIC under the framework of least squares estimation

被引：100

作者：

Banks, H. T. ^{[1
]}

Joyner, Michele L. ^{[2
]}

机构：

[1] North Carolina State Univ, Ctr Res Sci Computat, Raleigh, NC 27695 USA

[2] East Tennessee State Univ, Dept Math & Stat, Johnson City, TN 37614 USA

来源：

APPLIED MATHEMATICS LETTERS | 2017年 / 74卷

关键词：

Inverse problems; Akiake information content; Least squares estimation; Biological applications; MODEL SELECTION; INFORMATION CRITERION;

D O I：

10.1016/j.aml.2017.05.005

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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.

引用

页码：33 / 45

页数：13

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