Robust estimation of (partial) autocorrelation. (31st March 2015)

• Record Type: Journal Article
• Authors: Dürre, Alexander; Fried, Roland; Liboschik, Tobias
• Is Part Of: Wiley interdisciplinary reviews. Volume 7:Number 3(2015)
• Page Start: 205
• Page End: 222
• Publication Date: 2015-03-31
• Subjects: Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1351
• Languages: English
• ISSNs: 1939-5108

Abstract

<abstract abstract-type="main" id="wics1351-abs-0001"> <title>Abstract</title> <p id="wics1351-para-0001">The autocorrelation function (acf) and the partial autocorrelation function (pacf) are elementary tools of linear time series analysis. The sensitivity of the conventional sample acf and pacf to outliers is well known. We review robust estimators and evaluate their performances in different data situations considering Gaussian scenarios with and without outliers as well as times series with heavy tails in a simulation study. <italic>WIREs Comput Stat</italic> 2015, 7:205–222. doi: 10.1002/wics.1351</p> <p>For further resources related to this article, please visit the <ext-link ext-link-type="uri" xlink:href="http://wires.wiley.com/remdoi.cgi?doi=10.1002/wics.1351" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink">WIREs website</ext-link>.</p> <p>Conflict of interest: The authors have declared no conflicts of interest for this article.</p> </abstract>