Estimating the Inverse Autocorrelation Function from Outlier Contaminated Data
Posted: 30 May 2001
We show how a number of commonly used estimates of the inverse autocorrelation function can be modified to deal with outlier contaminated data. The robust analogues of the orthogonal and interpolation based techniques appear to be new, and provide an alternative to the robust autoregressive approach. We examine the performance of these techniques in a large scale numerical experiment. This shows significant improvements in performance in outlier contaminated data when robust techniques are used. While there was no uniformly best robust technique, our experiments support the use of the autoregressive approach to avoid catastrophic reductions in performance, and robust interpolation for short series corrupted by few outliers.
Keywords: Inverse autocorrelation, robust estimation, additive outlier, order selection
Suggested Citation: Suggested Citation