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Robust estimation of (partial) autocorrelation
Author(s) -
Dürre Alexander,
Fried Roland,
Liboschik Tobias
Publication year - 2015
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1351
Subject(s) - autocorrelation , partial autocorrelation function , outlier , estimator , series (stratigraphy) , mathematics , statistics , time series , gaussian , robust statistics , algorithm , computer science , autoregressive integrated moving average , physics , paleontology , quantum mechanics , biology
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. WIREs Comput Stat 2015, 7:205–222. doi: 10.1002/wics.1351 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Robust Methods Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data