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

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  • Fengkai Yang

Abstract

We proposed a robust mean change‐point estimation algorithm in linear regression with the assumption that the errors follow the Laplace distribution. By representing the Laplace distribution as an appropriate scale mixture of normal distribution, we developed the expectation maximization (EM) algorithm to estimate the position of mean change‐point. We investigated the performance of the algorithm through different simulations, finding that our methods is robust to the distributions of errors and is effective to estimate the position of mean change‐point. Finally, we applied our method to the classical Holbert data and detected a change‐point.

Suggested Citation

  • Fengkai Yang, 2014. "Robust Mean Change‐Point Detecting through Laplace Linear Regression Using EM Algorithm," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:856350
    DOI: 10.1155/2014/856350
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    References listed on IDEAS

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    1. Felipe Osorio & Manuel Galea, 2006. "Detection of a change-point in student-t linear regression models," Statistical Papers, Springer, vol. 47(1), pages 31-48, January.
    2. Jin-Guan Lin & Ji Chen & Yong Li, 2012. "Bayesian Analysis of Student t Linear Regression with Unknown Change-Point and Application to Stock Data Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 203-217, October.
    3. Holbert, Donald, 1982. "A Bayesian analysis of a switching linear model," Journal of Econometrics, Elsevier, vol. 19(1), pages 77-87, May.
    4. Ole E. Barndorff‐Nielsen & Neil Shephard, 2001. "Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 167-241.
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