Abstract
Robust recursive estimation provides considerable computational advantage over iterative robust regression estimation, especially for large and ordered (e.g., with time) data sets. The robust recursive estimates are less sensitive than recursive least squares to the outliers and structural shifts, and produce residuals which are more effective in constructing tests for detecting a shift. In this paper we consider a problem of detecting a shift in regression when it is masked by outliers, and summarize results of a simulation study comparing several tests and estimates of the change point.
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© 1986 Physica-Verlag, Heidelberg for IASC (International Association for Statistical Computing)
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Kuh, E., Samarov, A. (1986). Robust Recursive Estimation and Detection of Shifts in Regression. In: De Antoni, F., Lauro, N., Rizzi, A. (eds) COMPSTAT. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-46890-2_32
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DOI: https://doi.org/10.1007/978-3-642-46890-2_32
Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-0355-6
Online ISBN: 978-3-642-46890-2
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