A Hurst exponent estimator based on autoregressive power spectrum estimation with order selection
Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 2nd International Conference on Biomedical Engineering and Biotechnology, 11–13 October 2013, Wuhan, China
Affiliations: Department of Medical Informatics, Chung Shan Medical University and Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, 40201, Taiwan, ROC | Bachelor Program in Robotics, National Pingtung University of Education, Pingtung, 90003, Taiwan, ROC | Department of Computer Information and Network Engineering, Lunghwa University of Science and Technology, Taoyuan, 33306, Taiwan, ROC | Department of Applied Informatics and Multimedia, Asia University, Taichung, 41354, Taiwan, ROC | Department of Medical Informatics, Chung Shan Medical University, Taichung, 40201, Taiwan, ROC
Abstract: The discrete-time fractional Gaussian noise (DFGN) has been proven to be a regular process. According to Wold and Kolmogorov theorems, this process can be described as an autoregressive (AR) model of an infinite order. An estimator for the Hurst exponent based on autoregressive power spectrum estimation has been proposed, but without considering order selection. In this paper, six common order selection methods for the AR model were used to select appropriate orders of the AR model in order to raise the accuracy of estimating the Hurst exponent. Experimental results show that these six AR methods with considering order selection are more accurate than the original AR method without considering order selection.
