论文标题
非线性网络自动估计
Nonlinear Network Autoregression
论文作者
论文摘要
我们研究整数和连续有价值数据的时间序列网络的一般非线性模型。在已知网络的节点上测量的高维响应的向量通过使用平滑的链路函数在其滞后值和相邻节点的滞后值上进行了非线性回归。我们研究这种多元过程的稳定性条件，并在网络维度增加时发展准最大似然推断。此外，我们通过分别处理可识别和不可识别的参数来研究线性得分测试。在可识别性的情况下，测试统计量会收敛到卡方分布。当参数不可识别时，我们会开发一种超级型测试，其p值通过采用可行的结合和自举方法来充分近似。模拟和数据示例支持我们的发现。
We study general nonlinear models for time series networks of integer and continuous valued data. The vector of high dimensional responses, measured on the nodes of a known network, is regressed non-linearly on its lagged value and on lagged values of the neighboring nodes by employing a smooth link function. We study stability conditions for such multivariate process and develop quasi maximum likelihood inference when the network dimension is increasing. In addition, we study linearity score tests by treating separately the cases of identifiable and non-identifiable parameters. In the case of identifiability, the test statistic converges to a chi-square distribution. When the parameters are not-identifiable, we develop a supremum-type test whose p-values are approximated adequately by employing a feasible bound and bootstrap methodology. Simulations and data examples support further our findings.
