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

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

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) algori...

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Bibliographic Details
Main Author: Fengkai Yang
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2014/856350
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spelling doaj-036e39d8c880443f904f1156b6229a412020-11-24T23:47:49ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/856350856350Robust Mean Change-Point Detecting through Laplace Linear Regression Using EM AlgorithmFengkai Yang0School of Mathematics, Shandong University, Jinan 250100, ChinaWe 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.http://dx.doi.org/10.1155/2014/856350
collection DOAJ
language English
format Article
sources DOAJ
author Fengkai Yang
spellingShingle Fengkai Yang
Robust Mean Change-Point Detecting through Laplace Linear Regression Using EM Algorithm
Journal of Applied Mathematics
author_facet Fengkai Yang
author_sort Fengkai Yang
title Robust Mean Change-Point Detecting through Laplace Linear Regression Using EM Algorithm
title_short Robust Mean Change-Point Detecting through Laplace Linear Regression Using EM Algorithm
title_full Robust Mean Change-Point Detecting through Laplace Linear Regression Using EM Algorithm
title_fullStr Robust Mean Change-Point Detecting through Laplace Linear Regression Using EM Algorithm
title_full_unstemmed Robust Mean Change-Point Detecting through Laplace Linear Regression Using EM Algorithm
title_sort robust mean change-point detecting through laplace linear regression using em algorithm
publisher Hindawi Limited
series Journal of Applied Mathematics
issn 1110-757X
1687-0042
publishDate 2014-01-01
description 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.
url http://dx.doi.org/10.1155/2014/856350
work_keys_str_mv AT fengkaiyang robustmeanchangepointdetectingthroughlaplacelinearregressionusingemalgorithm
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