Stratified robust model selection reduces the impact of large residuals and overrepresented outliers in bootstrap samples but is computationally intensive when fitting iteratively-solved robust estimators across many candidate models. We propose FAMM, a Fast Approximate MM-estimator, implemented as a weighted least squares fit with weights derived from a full-data MM-estimator, to reduce this computational cost. Using extensive artificial simulations and applications to National Basketball Association data, we show that substituting the MM-estimator with FAMM preserves model selection performance while achieving a substantial computational speedup. Furthermore, we demonstrate that FAMM satisfies the required conditions for model selection consistency.

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