Robust Autocorrelation Estimation. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Chang, Christopher C.; Politis, Dimitris N.
• Is Part Of: Journal of computational and graphical statistics. Volume 25:Issue 1(2016)
• Page Start: 144
• Page End: 166
• Publication Date: 2016-01-02
• Subjects: Regression -- Robustness; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2014.969431
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract : In this article, we introduce a new class of robust autocorrelation estimators based on interpreting the sample autocorrelation function as a linear regression. We investigate the efficiency and robustness properties of the estimators that result from employing three common robust regression techniques. We discuss the construction of robust autocovariance and positive definite autocorrelation estimates, and their application to AR model fitting. We perform simulation studies with various outlier configurations to compare the different estimators.