Determining the order of an arm a model from outlier contaminated data

Abstract

We consider the problem of determining the order of an ARM A process from outlier contaminated data. We focus on four variants of the well known corner method derived from different estimating equations, see Choi (1992). These are modified to deal with outlier contaminated data using robust analogues of the autocorrelation function, inverse autocorrelation function, AR(∞) and MA(∞) representations. We evaluate our suggestions (which appear to be new) in a large scale numerical experiment where they out-perform their non-robust com¬petitors in outlier contaminated data. While there was no uniformly best robust procedure, our results support the use of the robust AR(∞) approach.

Keywords:

• Phidses Aildiiivc outliers
• AttM A. process: bootstrapping: model selection
• pattern recognition
• robust estimation