Robust estimation of bilinear time series models

Outliers in time series seriously affect conventional least squares (LS) procedures badly. We propose to study the problem of fitting a bilinear model to time series data in presence of additive outliers (AO). We investigate a modification of some robustified versions of methods used in linear time series models. A Monte Carlo study is performed to investigate the robustness properties of the proposed class of estimates.

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