Local influence analysis for Poisson autoregression with an application to stock transaction data
Local influence analysis for Poisson autoregression with an application to stock transaction data
复制标题
泊松自回归的局部影响分析及其在股票交易数据中的应用
DOI：
10.1111/stan.12071
复制
发表时间：
2016-02-01
影响因子：
1.5
通讯作者：
Shi, Lei
中科院分区：
文献类型：
--
作者：
Zhu, Fukang;Liu, Shuangzhe;Shi, Lei
在统计诊断和灵敏度分析中，局部影响方法发挥着重要作用，并且在某些情况下比其他方法具有一定的优势。在本文中，我们使用这种方法来研究时间序列的计数数据时，采用泊松自回归模型。我们考虑情况权重、尺度、数据和加性扰动方案，以获得它们相应的向量和导数矩阵，用于斜率和法向曲率的度量。在曲率诊断的基础上，我们采用逐步局部影响法来处理可能存在掩蔽效应的数据。最后，通过对一个股票交易数据集的分析，验证了本文的结论是有效的。
In statistical diagnostics and sensitivity analysis, the local influence method plays an important role and has certain advantages over other methods in several situations. In this paper, we use this method to study time series of count data when employing a Poisson autoregressive model. We consider case‐weights, scale, data, and additive perturbation schemes to obtain their corresponding vectors and matrices of derivatives for the measures of slope and normal curvatures. Based on the curvature diagnostics, we take a stepwise local influence approach to deal with data with possible masking effects. Finally, our established results are illustrated to be effective by analyzing a stock transactions data set.