English(EN) Deviance-style normalization for jointly overdispersed counts

引入稀疏计数数据的新归一化方法

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-24 17:36

一篇新论文介绍了一种用于分析稀疏、联合过度分散的计数矩阵的偏差风格归一化方法，该方法特别适用于测序等生化测定。所提出的狄利克雷-多项式（DM）零模型将计数向量视为固定总数的组合，并通过保持稀疏性来提供计算效率。这种方法可以扩展到有序和树状结构数据，为各种计数数据分析提供统一的残差族。 AI

影响这种统计方法可以改进生物数据的分析，并可能影响基于此类数据训练的AI模型。

排序理由该集群包含一篇详细介绍新统计方法的学术论文。[lever_c_demoted from research: ic=2 ai=0.4]

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

本文如何被排名

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arXiv stat.ML TIER_1 English(EN) · Akshay Balsubramani · 2026-06-25 04:00

联合过度分散计数的偏差风格归一化

arXiv:2606.26061v1 Announce Type: cross Abstract: We introduce a Dirichlet--multinomial (DM) deviance residualization for sparse, jointly overdispersed count matrices, the regime that dominates sequencing-based biochemical assays. The DM null treats each sample's count vector as …
arXiv stat.ML TIER_1 English(EN) · Akshay Balsubramani · 2026-06-24 17:36

联合过度分散计数的偏差风格归一化

We introduce a Dirichlet--multinomial (DM) deviance residualization for sparse, jointly overdispersed count matrices, the regime that dominates sequencing-based biochemical assays. The DM null treats each sample's count vector as a fixed-total composition with a single scalar con…