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Robust Regression with Density Power Divergence: Theory, Comparisons, and Data Analysis
Article

Robust Change Point Test for General Integer-Valued Time Series Models Based on Density Power Divergence

by 1,* and 2
1
Department of Statistics, Yeungnam University, Gyeongsan 38541, Korea
2
Department of Statistics, Seoul National University, Seoul 08826, Korea
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(4), 493; https://doi.org/10.3390/e22040493
Received: 17 February 2020 / Revised: 24 April 2020 / Accepted: 24 April 2020 / Published: 24 April 2020
In this study, we consider the problem of testing for a parameter change in general integer-valued time series models whose conditional distribution belongs to the one-parameter exponential family when the data are contaminated by outliers. In particular, we use a robust change point test based on density power divergence (DPD) as the objective function of the minimum density power divergence estimator (MDPDE). The results show that under regularity conditions, the limiting null distribution of the DPD-based test is a function of a Brownian bridge. Monte Carlo simulations are conducted to evaluate the performance of the proposed test and show that the test inherits the robust properties of the MDPDE and DPD. Lastly, we demonstrate the proposed test using a real data analysis of the return times of extreme events related to Goldman Sachs Group stock. View Full-Text
Keywords: integer-valued time series; one-parameter exponential family; minimum density power divergence estimator; density power divergence; robust change point test integer-valued time series; one-parameter exponential family; minimum density power divergence estimator; density power divergence; robust change point test
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MDPI and ACS Style

Kim, B.; Lee, S. Robust Change Point Test for General Integer-Valued Time Series Models Based on Density Power Divergence. Entropy 2020, 22, 493. https://doi.org/10.3390/e22040493

AMA Style

Kim B, Lee S. Robust Change Point Test for General Integer-Valued Time Series Models Based on Density Power Divergence. Entropy. 2020; 22(4):493. https://doi.org/10.3390/e22040493

Chicago/Turabian Style

Kim, Byungsoo, and Sangyeol Lee. 2020. "Robust Change Point Test for General Integer-Valued Time Series Models Based on Density Power Divergence" Entropy 22, no. 4: 493. https://doi.org/10.3390/e22040493

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