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- Title
Robust Change Point Test for General Integer-Valued Time Series Models Based on Density Power Divergence.
- Authors
Kim, Byungsoo; Lee, Sangyeol
- Abstract
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.
- Subjects
CHANGE-point problems; MONTE Carlo method; POWER density; GOLDMAN Sachs Asset Management (Company); TIME series analysis; EXPONENTIAL families (Statistics)
- Publication
Entropy, 2020, Vol 22, Issue 4, p493
- ISSN
1099-4300
- Publication type
Article
- DOI
10.3390/e22040493