A Non-Gaussian Model for Time Series with Pulses
提出一种非高斯自回归模型，用于描述偶尔大幅增长（脉冲）且脉冲间呈指数衰减的时间序列，并开发了基于似然的推断方法，应用于内分泌学数据。
Abstract A non-Gaussian autoregressive-like model is presented for time series that exhibit occasional large increases in value, termed pulses, and exponential decay between pulses. The model differs from a first-order autoregressive process in its incorporation of feedback between the distribution of the current innovation and the history of the process. Likelihood-based methods of inference for the model are developed, and an application to endocrinological data is given.