In this paper an algorithm is developed, which aims to find all FPCs of a dataset corresponding to well separated linear regression subpopulations. Its ability to find such subpopulations under the occurence of outliers is compared to methods based on ML-estimation of mixture models by means of a simulation study. Furthermore, FPC analysis is applied to a real dataset.
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Hennig, C. Fixed Point Clusters for Linear Regression: Computation and Comparison . J. of Classification 19, 249–276 (2002). https://doi.org/10.1007/s00357-001-0045-7
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DOI: https://doi.org/10.1007/s00357-001-0045-7