STUDIES IN ASTRONOMICAL TIME SERIES ANALYSIS. VI. BAYESIAN BLOCK REPRESENTATIONS
STUDIES IN ASTRONOMICAL TIME SERIES ANALYSIS. VI. BAYESIAN BLOCK REPRESENTATIONS
复制标题
DOI：
10.1088/0004-637x/764/2/167
复制
发表时间：
2012-07
期刊：
影响因子：
--
通讯作者：
J. Scargle;J. Norris;B. Jackson;J. Chiang
中科院分区：
文献类型：
--
作者：
J. Scargle;J. Norris;B. Jackson;J. Chiang
本文讨论了时间序列和其他形式的序列数据中的局部变异性的检测和表征问题。目标是识别和表征统计上显著的变化，同时抑制不可避免的破坏性观测误差。我们提出了一个简单的非参数建模技术和算法实现它的改进和广义版本的贝叶斯块，发现最佳分割的数据在观察区间。该算法的结构允许其用于实时触发模式或回顾模式。最大似然或边际后验函数来衡量模型的适应性提出的事件，装箱计数，并在任意时间与已知的误差分布测量。所解决的问题包括那些与数据缺口，变量暴露，分段线性和分段指数表示，多元时间序列数据，方差分析，数据上的循环，其他数据模式，分散的数据。模拟提供的证据表明，弱信号的检测效率是接近理论渐近极限阿里亚斯-卡斯特罗等人。在可再生研究的精神，所有的代码和数据，必要的复制本文中的所有数字包括作为补充材料。
This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it—an improved and generalized version of Bayesian Blocks—that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piecewise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by Arias-Castro et al. In the spirit of Reproducible Research all of the code and data necessary to reproduce all of the figures in this paper are included as supplementary material.