Robust Autocorrelation Estimation
نویسندگان
چکیده
In this paper, 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. Construction of robust autocovariance and positive definite autocorrelation estimates is discussed, as well as application to AR model fitting. Simulation studies with various outlier configurations are performed in order to compare the different estimators.
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