# Sequential estimation of quantiles with applications to A/B-testing and best-arm identification

## TL;DR

This paper introduces confidence sequences for quantiles that are valid over time, providing explicit, tight intervals and applications to bandit problems, outperforming existing methods in sample efficiency.

## Contribution

It develops the first explicit confidence sequences for quantiles with optimal shrinking rates and applies them to improve bandit algorithms for best-arm identification.

## Key findings

- Confidence sequences for quantiles with optimal $oldsymbol{rac{ ext{log log t}}{ ext{t}}}$ rate.
- Uniform concentration inequality extending Dvoretzky-Kiefer-Wolfowitz.
- Sample complexity reduction by a factor of 5 to 50 in bandit applications.

## Abstract

We propose confidence sequences -- sequences of confidence intervals which are valid uniformly over time -- for quantiles of any distribution over a complete, fully-ordered set, based on a stream of i.i.d. observations. We give methods both for tracking a fixed quantile and for tracking all quantiles simultaneously. Specifically, we provide explicit expressions with small constants for intervals whose widths shrink at the fastest possible $\sqrt{t^{-1} \log\log t}$ rate, along with a non-asymptotic concentration inequality for the empirical distribution function which holds uniformly over time with the same rate. The latter strengthens Smirnov's empirical process law of the iterated logarithm and extends the Dvoretzky-Kiefer-Wolfowitz inequality to hold uniformly over time. We give a new algorithm and sample complexity bound for selecting an arm with an approximately best quantile in a multi-armed bandit framework. In simulations, our method requires fewer samples than existing methods by a factor of five to fifty.