Mixture Periodic Autoregressive Moving Average Model with Application to PM10 Concentrations

摘要：

We generalize the Mixture Periodic Autoregressive (MPAR) model introduced by Shao to the Mixture Periodic Autoregressive Moving Average (MPARMA) model for the modelling nonlinear time series. The stationarity is derived. The estimation is done via EM algorithm and the model selection criterion is given. The model is illustrated by analyzing the particulate matter concentrations in Cleveland, OH.

关键词： periodically correlated time series mixture pe riodic autoregressive moving average models em algorithm BIC

作者: Huizhan Wang Fangan Deng

作者单位: Department of Mathematics Shaanxi University of Technology Hanzhong, Shaanxi 723000, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 45-49

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）