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Journal of Applied Mathematics
Volume 2014, Article ID 856350, 9 pages
Research Article

Robust Mean Change-Point Detecting through Laplace Linear Regression Using EM Algorithm

1School of Mathematics, Shandong University, Jinan 250100, China
2School of Mathematics and Statistics, Shandong University, Weihai 264209, China

Received 20 July 2014; Revised 5 September 2014; Accepted 5 September 2014; Published 4 November 2014

Academic Editor: Zhihua Zhang

Copyright © 2014 Fengkai Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [3 citations]

The following is the list of published articles that have cited the current article.

  • Kang-Ping Lu, and Shao-Tung Chang, “Detecting change-points for shifts in mean and variance using fuzzy classification maximum likelihood change-point algorithms,” Journal of Computational and Applied Mathematics, vol. 308, pp. 447–463, 2016. View at Publisher · View at Google Scholar
  • Fengkai Yang, and Haijing Yuan, “A non-iterative posterior sampling algorithm for Laplace linear regression model,” Communications in Statistics: Simulation and Computation, vol. 46, no. 3, pp. 2488–2503, 2017. View at Publisher · View at Google Scholar
  • Kang-Ping Lu, and Shao-Tung Chang, “A fuzzy classification approach to piecewise regression models,” Applied Soft Computing, 2018. View at Publisher · View at Google Scholar