Applicability evaluation of Akaike’s Bayesian information criterion to covariance modeling in the cross-section adjustment method
Applicability evaluation of Akaike’s Bayesian information criterion to covariance modeling in the cross-section adjustment method
复制标题
赤池贝叶斯信息准则对截面平差法协方差建模的适用性评价
DOI：
10.1051/epjconf/202328100008
复制
发表时间：
2023
影响因子：
--
通讯作者：
Akio Yamamoto
中科院分区：
文献类型：
--
作者：
Shuhei Maruyama;Tomohiro Endo;Akio Yamamoto
研究了赤池贝叶斯信息准则（ABIC）在截面调整方法中协方差建模中的适用性。在传统的截面平差方法中，假设协方差矩阵为真。然而，这种假设并不总是合适的。为了提高截面调整方法的可靠性，引入了度量ABIC的协方差模型估计，并通过简单的数值实验研究了ABIC的性能。本文推导了有效评估 ABIC 的公式，该公式由较低秩矩阵表示，以便在实际计算时间内进行大样本数值实验。从数值实验的结果来看，已经证实ABIC倾向于选择超参数较少、估计误差方差较小的协方差模型。然而，人们还发现，当协方差模型的结构与真实模型的结构相差甚远时，ABIC 的这一理想特性就会丧失。
The applicability of Akaike’s Bayesian Information Criterion (ABIC) to the covariance modeling in the cross-section adjustment method has been investigated. In the conventional cross-section adjustment method, the covariance matrices are assumed to be true. However, this assumption is not always appropriate. To improve the reliability of the cross-section adjustment method, the estimation of the covariance model using the metric ABIC has been introduced, and the performance of ABIC has been investigated through simple numerical experiments. This paper derives the formula to efficiently evaluate ABIC which is represented by a lower rank matrix to enable numerical experiments with large samples in a realistic computation time. From the results of the numerical experiments, it has been confirmed that ABIC tends to select a covariance model with fewer hyperparameters and a smaller variance for the estimation error. However, it has also been found that this desirable property of ABIC will be lost when the structure of the covariance model is far from the true one.
提供核数据和协方差数据调整反馈以改进核数据文件的方法和途径：NEA WPEC 小组 39 的主要发现
影响因子：
--
作者：
M. Salvatores;G. Palmiotti
通讯作者：
G. Palmiotti
积分参数调整的边际似然优化的开发与应用
影响因子：
1.9
作者：
D. Siefman;M. Hursin;G. Schnabel;H. Sjöstrand
通讯作者：
H. Sjöstrand