Detecting changes in linear regression models with skew normal errors

In this article, we discuss a linear regression change-point model with skew normal errors. We propose a testing procedure, based on a modified version of the Schwarz information criterion, which is named the modified information criterion (MIC) to locate change points in such a linear regression mo...

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Bibliographic Details
Published inRandom operators and stochastic equations Vol. 26; no. 1; pp. 1 - 10
Main Authors Said, Khamis K., Ning, Wei, Tian, Yubin
Format Journal Article
LanguageEnglish
Published Berlin De Gruyter 01.03.2018
Walter de Gruyter GmbH
Subjects
62F03
62J05
Change detection
Change point detection
Computer simulation
Criteria
Economic models
Empirical analysis
linear regression model
Normal distribution
Regression analysis
Regression models
Schwarz information criterion
skew normal distribution
Statistical analysis
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Summary:In this article, we discuss a linear regression change-point model with skew normal errors. We propose a testing procedure, based on a modified version of the Schwarz information criterion, which is named the modified information criterion (MIC) to locate change points in such a linear regression model. Due to the difficulty of derivation of the asymptotic null distribution of the associated test statistic analytically, the empirical critical values at different significance levels are approximated through simulations. Simulations have also been conducted under different changes among parameters of interest with various sample sizes to investigate the performance of the proposed test. Such a procedure has been applied on a NASA data to illustrate the detecting process.
ISSN:0926-6364
1569-397X
DOI:10.1515/rose-2018-0001