Pseudolikelihood estimators for graphical models: existence and uniqueness
图模型伪似然估计量的存在性与唯一性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。
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：摘要属于图模型估计的通用理论，未明确经济网络、因果识别或其他所列应用方向。
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
图模型伪似然估计量的存在性与唯一性
- 研究问题
- 高维小样本图模型中，估计量何时存在并具有唯一性；作者研究两类伪似然方法所需样本规模及其与高斯似然结果的联系。
- 方法与思路
- 作者针对文献中的两类伪似然方法开展理论分析，刻画估计量存在性相关的样本条件，并联系相应高斯模型的结论。
- 材料陈述的贡献
- 摘要称，论文对长期未解决的伪似然估计存在性问题给出精确回答，并建立两类方法的结果与高斯对应结果之间的联系。
- 阅读关注点
- 应核对两类伪似然的具体定义、存在性与唯一性的分别结论，以及图结构、样本位置和维度如何决定所需样本条件。
- 阅读边界
- 仅据所给 Crossref 摘要，无法给出最小样本量或唯一性判据，也不能把估计量存在解释为有限样本估计质量良好。
原文摘要
Summary Graphical and sparse (inverse) covariance models have found widespread use in modern sample-starved high-dimensional applications. A part of their wide appeal stems from the significantly low sample sizes required for existence of the estimators, especially in comparison with the classical full covariance model. For undirected Gaussian graphical models, the minimum sample size required for the existence of maximum likelihood estimators had been an open question for almost half a century, and has recently been addressed in a series of works (Uhler, 2012; Ben-David, 2015; Gross & Sullivant, 2018). The very same question for pseudolikelihood estimators has remained unsolved ever since their introduction in the 1970s. Pseudolikelihood estimators have recently received renewed attention as they impose fewer restrictive assumptions and have better computational tractability, improved statistical performance and appropriateness in high-dimensional applications, thus renewing interest in this longstanding problem. In this paper, we undertake a comprehensive study of this open problem within the context of the two classes of pseudolikelihood methods proposed in the literature. We provide a precise answer to this question for both pseudolikelihood approaches and relate the corresponding solutions to their Gaussian counterparts.