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In the previous post, I described the idea of using quantile estimators based on k order statistics. Potentially, such estimators could be more robust than estimators based on all samples elements (like Harrell-Davis, Sfakianakis-Verginis, or Navruz-Özdemir) and more statistically efficient than traditional quantile estimators (based on 1 or 2 order statistics). Moreover, we should be able to control this trade-off based on the business requirements (e.g., setting the desired breakdown point). T...