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分位数网络向量自回归中的双向同质性分组AI 解读

We propose a two-way grouped network quantile (TGNQ) autoregression model for time series data observed on networks with substantial heterogeneity and complex directional interactions. Motivated by prior studies on network effects in directed networks, our model assigns each node two latent group memberships to flexibly capture asymmetric and heterogeneous interactions among users. These memberships, along with model parameters, can be consistently estimated using the…

具有潜在组结构的矩阵值网络自回归模型AI 解读

Matrix-valued time series data are frequently observed in a broad range of areas and have attracted great attention recently. In this work, we model network effects for high dimensional matrix-valued time series data in a matrix autoregression framework. To characterize the potential heterogeneity of the subjects and handle the high dimensionality simultaneously, we assume that each subject has a latent group label, which enables us to cluster the subject into the…

具有潜在群组结构的多关系网络自回归模型AI 解读

Multi-relational networks among entities are frequently observed in the era of big data. Quantifying the effects of multiple networks have attracted significant research interest recently. In this work, we model multiple network effects through an autoregressive framework for tensor-valued time series. To characterize the potential heterogeneity of the networks and handle the high dimensionality of the time series data simultaneously, we assume a separate group structure for…

时空点过程的复合似然推断AI 解读

The dynamics of a rain forest is extremely complex involving births, deaths and growth of trees with complex interactions between trees, animals, climate, and environment. We consider the patterns of recruits (new trees) and dead trees between rain forest censuses. For a current census we specify regression models for the conditional intensity of recruits and the conditional probabilities of death given the current trees and spatial covariates. We estimate regression…

异质节点网络向量自回归模型的联合估计与分组识别AI 解读

Individuals or companies in a large social or financial network often display rather heterogeneous behaviors for various reasons. In this work, we propose a network vector autoregressive model with a latent group structure to model heterogeneous dynamic patterns observed from network nodes, for which group-wise network effects and timeinvariant fixed-effects can be naturally incorporated. In our framework, the model parameters and network node memberships can be…