论文标题
具有多元外源协变量的一般整数值估算时间序列的密度功率发散
Density power divergence for general integer-valued time series with multivariate exogenous covariate
论文作者
论文摘要
在本文中，我们研究了一类Integer价值计值时间序列模型的强大估计方法。 该过程的条件分布属于广泛的分布类别，与经典自回归框架不同，该过程的条件平均值还取决于一些多元外源协变量。 我们根据最小密度功率发散得出了强大的推理过程。 在某些规律性条件下，我们确定所提出的估计量是一致的，并且渐近地正常。 进行仿真实验以说明估计量的经验性能。还提供了股票Ericsson B每分钟交易数量的申请。
In this article, we study a robust estimation method for a general class of integer-valued time series models. The conditional distribution of the process belongs to a broad class of distribution and unlike classical autoregressive framework, the conditional mean of the process also depends on some multivariate exogenous covariate. We derive a robust inference procedure based on the minimum density power divergence. Under certain regularity conditions, we establish that the proposed estimator is consistent and asymptotically normal. Simulation experiments are conducted to illustrate the empirical performances of the estimator. An application to the number of transactions per minute for the stock Ericsson B is also provided.
