Tree-Embedded Bayesian Factor Models for Multidimensional Categorical Distributions
多维分类分布的树嵌入贝叶斯因子模型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：摘要研究一般分组分类分布的贝叶斯因子建模，未涉及宏观或金融因子，也未估计分布处理效应，现有分类证据不足。
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…
多维分类分布的树嵌入贝叶斯因子模型
- 研究问题
- 面对不同群体或地点的多维分类分布，如何以低维共同结构描述其共性与异质性，并适应缺少明显聚类的分布数据？
- 方法与思路
- 作者以树变换将分布嵌入欧氏空间，构建可自适应有效因子数的贝叶斯无限因子模型，并用 SAR 型先验扩展空间依赖。
- 材料陈述的贡献
- 摘要称模型可平滑估计单变量及多变量分布结构，真实人口数据实验中，在若干平滑空间变化情景及小样本下优于比较模型。
- 阅读关注点
- 应核对树结构选择及变换的信息保留、因子与空间先验的设定、有效因子数判定，以及模型比较的指标和数据划分。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明树构造、先验细节和比较数值，优势仅能理解为所述实验中的报告。
原文摘要
Analyzing data collected from multiple observational units to estimate common and heterogeneous structures through a hierarchical model is a central task in Bayesian inference, and to this end, Bayesian factor models are one of the most widely used tools for this purpose. In this paper, we propose a novel Bayesian latent factor model for categorical distributions from grouped data, providing a parsimonious model for describing many observed distributions through lower-dimensional structures. Grouped data arise in a wide range of applications in social science, for example, distributions of age composition and income observed across locations. In these contexts, standard mixture models can be inefficient because the distributions do not necessarily exhibit clear clustering structures, and the distributions can be more accurately approximated as a combination of lower-dimensional characteristics. To analyze distribution-valued data with the Bayesian factor analysis, we adopt a tree-based transformation that embeds distributions into a Euclidean space and construct a Bayesian latent factor model in the transformed space. We develop the hierarchical model by incorporating the infinite factor model, which can adaptively estimate the number of effective factors. In addition, we propose its generalization by incorporating a spatial dependence by introducing a prior based on a SAR model. The proposed model provides smooth estimates of multivariate distributional structures, because once a tree-based transformation is applied, both univariate and multivariate distributions are essentially treated as the same Euclidean vectors. Through numerical experiments using real population data, we demonstrate that the proposed model outperforms existing parametric and Bayesian nonparametric models in various scenarios involving smooth spatial variations, especially under small sample sizes.