Computing Extremely Accurate Quantiles Using t-Digests

Abstract

We present on-line algorithms for computing approximations of rank-based statistics that give high accuracy, particularly near the tails of a distribution, with very small sketches. Notably, the method allows a quantile $q$ to be computed with an accuracy relative to $\max (q,1-q)$ rather than absolute accuracy as with most other methods. This new algorithm is robust with respect to skewed distributions or ordered datasets and allows separately computed summaries to be combined with no loss in accuracy. An open-source Java implementation of this algorithm is available from the author. Independent implementations in Go and Python are also available.

22 pages, 10 figures

Cited by in corpus (7)

• A Survey of Approximate Quantile Computation on Large-scale Data (Technical Report)
• Continual Learning in Practice
• Asymptotic Singular Value Distribution of Linear Convolutional Layers
• The Size of a $t$-Digest
• Conservation of the $t$-digest Scale Invariant
• Storyboard: Optimizing Precomputed Summaries for Aggregation
• Asymmetric scale functions for $t$-digests