整数值时间序列的拟似然推断

基于推广的负二项稀疏算子利用预设新息过程分布法构造一个一元 INAR(1) 模型,给出了模型的概率性质并利用拟似然估计方法对模型进行了参数估计，同时也考虑了最小二乘法、极大似然估计方法。通过数值模拟评估了这些估计方法的有效性，并应用实际数据给出模型的应用，通过比较得出基于推广的负二项稀疏算子带有几何新息过程的INAR(1)是更适合数据的模型。

Quasi-likelihood Inference for Integer-valued Time Series

Based on extended negative binomial thinning operator, an integer-valued INAR(1) model is constructed by prespecifying the distribution for the innovation process. The probabilistic properties of the model are given and the parameters of the model are estimated by using the quasi likelihood estimation method, and least squares and maximum likelihood estimation methods are also considered. The validity of these estimation methods is evaluated by numerical simulation, and the application of the model is given based on the actual data. Through comparison, it is concluded that the INAR (1) based on the extended negative binomial thinning operator with the innovation process of geometric distribution is a more suitable model for the data.