Development of spectral decomposition based on Bayesian information criterion with estimation of confidence interval
Development of spectral decomposition based on Bayesian information criterion with estimation of confidence interval
复制标题
基于贝叶斯信息准则和置信区间估计的谱分解的发展
DOI：
10.1080/14686996.2020.1773210
复制
发表时间：
2020
影响因子：
5.5
通讯作者：
Okada Masato
中科院分区：
文献类型：
--
作者：
Shinotsuka Hiroshi;Nagata Kenji;Yoshikawa Hideki;Mototake Yoh-Ichi;Shouno Hayaru;Okada Masato
我们开发了一个自动峰值拟合算法，使用贝叶斯信息准则（BIC）拟合方法与置信区间估计的谱分解。首先，采用贝叶斯交换蒙特卡罗方法对各种人工光谱数据进行光谱分解，估计拟合参数的置信区间。从结果中，一个近似的模型公式，表示参数的置信区间和峰间距离和信噪比之间的关系，推导。接下来，对于真实的光谱数据，我们比较了使用贝叶斯交换蒙特卡罗方法获得的每个峰参数的置信区间与通过模型选择函数和建议的近似公式的BIC拟合获得的置信区间。因此，我们确认，使用这两种方法获得的参数置信区间一致。因此，不仅可以通过BIC拟合简单地估计适当的峰数，而且可以获得拟合参数的置信区间。
We develop an automatic peak fitting algorithm using the Bayesian information criterion (BIC) fitting method with confidence-interval estimation in spectral decomposition. First, spectral decomposition is carried out by adopting the Bayesian exchange Monte Carlo method for various artificial spectral data, and the confidence interval of fitting parameters is evaluated. From the results, an approximated model formula that expresses the confidence interval of parameters and the relationship between the peak-to-peak distance and the signal-to-noise ratio is derived. Next, for real spectral data, we compare the confidence interval of each peak parameter obtained using the Bayesian exchange Monte Carlo method with the confidence interval obtained from the BIC-fitting with the model selection function and the proposed approximated formula. We thus confirm that the parameter confidence intervals obtained using the two methods agree well. It is therefore possible to not only simply estimate the appropriate number of peaks by BIC-fitting but also obtain the confidence interval of fitting parameters.