arXiv (All)
open access
Finite-Sample Distribution Theory and Efficient Large-Scale Inference for Online Quantile Regression
This paper studies online quantile regression for large-scale and streaming data using Stochastic SubGradient Descent (SSGD) with constant learning rates. Classical offline inference for quantile regression is computationally and memory intensive. Existing works of online inference for quantile regr
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- From Spectra to Joint Schedules in LLM Pre-training: 3+3(+2) Scaling-Law Regimes#1154414
- From Spectra to Joint Schedules in LLM Pre-training: 3+3(+2) Scaling-Law Regimes#1136829
- Does Step Law Transfer to Small-Scale Language Models? An Empirical Recalibration Below 59M Parameters#1064125
- Implicit Target Shift in Online Learning: Characterization and Correction#1198916
- Large-sample inference for nonparametric regression with dependent errors#176885
Record · ID 1202288
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive