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New SGD method enables online quantile estimation with theoretical guarantees

By PulseAugur Editorial · [1 sources] ·

This paper introduces a novel smoothed stochastic gradient descent (SGD) algorithm for online quantile estimation. The method ensures estimates remain monotone with respect to the quantile level throughout the streaming data process. The research provides theoretical guarantees on tail probability bounds and offers an online multiplier bootstrap for valid simultaneous inference across coordinates and quantile levels. AI

IMPACT Introduces a novel statistical method for online quantile estimation, potentially improving real-time data analysis in machine learning applications.

RANK_REASON Academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SGD method enables online quantile estimation with theoretical guarantees

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Academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Likai Chen, Georg Keilbar, Wei Biao Wu ·

    Online simultaneous inference for quantiles via smoothed stochastic gradient descent

    arXiv:2505.13299v2 Announce Type: replace Abstract: This paper considers the estimation of quantiles via a smoothed version of the stochastic gradient descent (SGD) algorithm. By smoothing the score function with a bandwidth tied to the learning rate, we obtain estimates that are…

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