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New SLSE-FRS framework improves linear model estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called Sequential Least-Squares Estimators with Fast Randomized Sketching (SLSE-FRS) for estimating large-scale linear statistical models. This method combines Sketch-and-Solve and Iterative-Sketching techniques to iteratively refine parameter estimates by solving increasingly precise sketched subproblems. Numerical experiments indicate that SLSE-FRS surpasses current state-of-the-art methods like Preconditioned Conjugate Gradient and Iterative Double Sketching in performance. AI

IMPACT Introduces a novel algorithmic approach for improving the efficiency and precision of linear statistical model estimation.

RANK_REASON The cluster contains a research paper detailing a new statistical estimation framework. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

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

New SLSE-FRS framework improves linear model estimation

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The cluster contains a research paper detailing a new statistical estimation framework. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Guan-Yu Chen, Dong-Yue Xie, Xi Yang ·

    Sequential Least-Squares Estimators with Fast Randomized Sketching for Linear Statistical Models

    arXiv:2509.06856v2 Announce Type: replace Abstract: We propose a novel randomized framework for the estimation problem of large-scale linear statistical models, namely Sequential Least-Squares Estimators with Fast Randomized Sketching (SLSE-FRS), which integrates Sketch-and-Solve…

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