.png){kind=icon}
返回
A General Pairwise Comparison Model for Extremely Sparse Networks
.png){kind=icon}
OA
AI
DOI：10.1080/01621459.2022.2053137.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Statistical estimation using pairwise comparison data is an effective approach to analyzing large-scale sparse networks. In this article, we propose a general framework to model the mutual interactions in a network, which enjoys ample flexibility in terms of model parameterization. Under this setup, we show that the maximum likelihood estimator for the latent score vector of the subjects is uniformly consistent under a near-minimal condition on network sparsity. This condition is sharp in terms of the leading order asymptotics describing the sparsity. Our analysis uses a novel chaining technique and illustrates an important connection between graph topology and model consistency. Our results guarantee that the maximum likelihood estimator is justified for estimation in large-scale pairwise comparison networks where data are asymptotically deficient. Simulation studies are provided in support of our theoretical findings. Supplementary materials for this article are available online.
Keyword:
Entry-wise error
Graph topology
Maximum likelihood estimation
Sparsity
Uniform consistency
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
机构
引用论文
分享收藏分享收藏Coupling energy metabolism with a mechanism to support brain-derived neurotrophic factor-mediated synaptic plasticity.png)
Neuroscience
OAAI
NeuroscienceIF0
OAAI分享收藏
查看更多内容