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New SLIM algorithm offers scalable inference for complex nonlinear GMM models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SLIM, a novel stochastic approximation algorithm designed for nonlinear Generalized Method of Moments (GMM) models with numerous nonseparable moments and parameters. This algorithm is proven to be consistent and asymptotically normal, even without a consistent initial estimator or a globally convex GMM criterion. SLIM demonstrates significant computational gains and scalability, as evidenced by experiments on a nonlinear demand system with over half a million observations and an application involving millions of college graduates on LinkedIn. AI

IMPACT This research introduces a scalable algorithm for complex statistical models, potentially impacting fields that rely on large-scale data analysis and inference.

RANK_REASON The cluster contains an academic paper detailing a new algorithm and its theoretical results. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

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

New SLIM algorithm offers scalable inference for complex nonlinear GMM models

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The cluster contains an academic paper detailing a new algorithm and its theoretical results. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Xiaohong Chen, Min Seong Kim, Sokbae Lee, Myung Hwan Seo, Myunghyun Song, Ling Zhong ·

    SLIM: Stochastic Learning and Inference in Nonlinear GMM Models

    arXiv:2510.20996v4 Announce Type: replace-cross Abstract: We propose SLIM (Stochastic Learning and Inference in overidentified Models), a new scalable stochastic approximation algorithm for nonlinear GMMs with many nonseparable moments and parameters. SLIM is shown to be consiste…

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