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Test for parameter change in the presence of outliers: the density power divergence-based approach
Journal of Statistical Computation and Simulation ( IF 1.1 ) Pub Date : 2020-11-10 , DOI: 10.1080/00949655.2020.1842407
Junmo Song 1 , Jiwon Kang 2
Affiliation  

This study considers the problem of testing for a parameter change in the presence of outliers. For this, we propose a robust test using the objective function of minimum density power divergence estimator (MDPDE) by Basu et al. (Biometrika, 1998), and then derive its limiting null distribution. Our test procedure can be naturally extended to any parametric model to which MDPDE can be applied. To illustrate this, we apply our test procedure to GARCH models. We demonstrate the validity and robustness of the proposed test through a simulation study. In a real data application to the Hang Seng index, our test locates some change-points that are not detected by the previous tests such as the score test and the residual-based CUSUM test.

中文翻译:

在存在异常值的情况下测试参数变化:基于密度功率发散的方法

本研究考虑了在存在异常值的情况下测试参数变化的问题。为此,我们使用 Basu 等人的最小密度功率散度估计器 (MDPDE) 的目标函数提出了一个稳健的测试。(Biometrika, 1998),然后推导出它的极限零分布。我们的测试程序可以自然地扩展到任何可以应用 MDPDE 的参数模型。为了说明这一点,我们将我们的测试程序应用于 GARCH 模型。我们通过模拟研究证明了所提议测试的有效性和稳健性。在恒生指数的实际数据应用中,我们的测试定位了一些以前的测试(例如分数测试和基于残差的 CUSUM 测试)没有检测到的变化点。
更新日期:2020-11-10
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