Count network autoregression
研究了计数数据在网络结构下的自回归模型，提出了保证模型稳定性和有效统计推断的条件，并证明拟似然估计的一致性和渐近正态性。
We consider network autoregressive models for count data with a non‐random neighborhood structure. The main methodological contribution is the development of conditions that guarantee stability and valid statistical inference for such models. We consider both cases of fixed and increasing network dimension and we show that quasi‐likelihood inference provides consistent and asymptotically normally distributed estimators. The article is complemented by simulation results and a data example.