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Deutsch(DE) Generalized Engression Models

新的广义回归模型为混合类型数据提供统一方法

研究人员推出广义回归模型(Generalized Engression Models),这是一个新颖的非参数分布回归框架,旨在处理具有混合数据类型的多变量结果。这种统一的方法建立在深度生成模型engression的基础上,并结合了特定数据类型的链接函数和用于基于梯度的训练的平滑扰动。这些模型在模拟和应用中表现出强大的性能,在边际得分上可与特定类型模型相媲美,同时提高了联合分布的准确性,并在特定领域超越了最先进的模型。 AI

影响 为处理统计建模中复杂、混合类型的数据分布引入了一个统一的框架。

排序理由 该集群描述了在arXiv上发布的新统计方法。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的广义回归模型为混合类型数据提供统一方法

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该集群描述了在arXiv上发布的新统计方法。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 Deutsch(DE) · Xinwei Shen, Zijian Guo, Francis Bach ·

    广义回归模型

    arXiv:2610.01823v1 Announce Type: cross Abstract: We consider estimating the conditional distribution of a multivariate outcome given covariates when its coordinates may be continuous, binary, categorical, ordinal or rankings, and are conditionally dependent on one another. Diffe…