High-Dimensional Overdispersed Generalized Factor Model with Application to Single-Cell Sequencing Data Analysis
高维过度离散广义因子模型及单细胞测序应用AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接提出高维广义因子模型，并研究潜在因子估计与因子数量选择。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
高维过度离散广义因子模型及单细胞测序应用
- 研究问题
- 如何对存在过度离散的高维混合类型数据建立非线性因子模型，并在复杂潜在结构下兼顾估计准确性和计算效率？
- 方法与思路
- 作者在广义因子模型中加入额外误差项，以结合拉普拉斯和泰勒近似的变分 EM 估计，并用奇异值比选择因子数。
- 材料陈述的贡献
- 摘要称算法具有收敛性质，模拟中估计精度和效率优于比较方法，并提供两个基因组应用及 R 包实现。
- 阅读关注点
- 应核对混合变量的分布设定、额外误差与因子的可区分性，以及变分近似、算法收敛和因子数选择各自的保证。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未说明收敛保证的具体含义，模拟优势也不能视为普遍优越性。
原文摘要
The current high-dimensional linear factor models fail to account for the different types of variables, while high-dimensional nonlinear factor models often overlook the overdispersion present in mixed-type data. However, overdispersion is prevalent in practical applications, particularly in fields like biomedical and genomics studies. To address this practical demand, we propose an overdispersed generalized factor model (OverGFM) for performing high-dimensional nonlinear factor analysis on overdispersed mixed-type data. Our approach incorporates an additional error term to capture the overdispersion that cannot be accounted for by factors alone. However, this introduces significant computational challenges due to the involvement of two high-dimensional latent random matrices in the nonlinear model. To overcome these challenges, we propose a novel variational EM algorithm that integrates Laplace and Taylor approximations. This algorithm provides iterative explicit solutions for the complex variational parameters and is proven to possess excellent convergence properties. We also develop a criterion based on the singular value ratio to determine the optimal number of factors. Numerical results demonstrate the effectiveness of this criterion. Through comprehensive simulation studies, we show that OverGFM outperforms state-of-the-art methods in terms of estimation accuracy and computational efficiency. Furthermore, we demonstrate the practical merit of our method through its application to two datasets from genomics. To facilitate its usage, we have integrated the implementation of OverGFM into the R package GFM.