论文标题
部分可观测时空混沌系统的无模型预测
Statistical Analysis of Multi-Relational Network Recovery
论文作者
论文摘要
在本文中，我们为大规模多关系网络的一类潜在变量模型开发了渐近理论。特别是，当网络的大小倾向于无穷大时，我们为（受惩罚）最大似然估计器建立一致性结果和渐近误差界限。基本技术是通过对随机场的大偏差分析来开发最大似然估计器约束的非质子误差。我们还表明，这些估计量在最小风险方面几乎是最佳的。
In this paper, we develop asymptotic theories for a class of latent variable models for large-scale multi-relational networks. In particular, we establish consistency results and asymptotic error bounds for the (penalized) maximum likelihood estimators when the size of the network tends to infinity. The basic technique is to develop a non-asymptotic error bound for the maximum likelihood estimators through large deviations analysis of random fields. We also show that these estimators are nearly optimal in terms of minimax risk.
