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Poisson QMLE for change-point detection in general integer-valued time series models
- Source :
- Metrika. 85:373-403
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- We consider together the retrospective and the sequential change-point detection in a general class of integer-valued time series. The conditional mean of the process depends on a parameter $$\theta ^*$$ which may change over time. We propose procedures which are based on the Poisson quasi-maximum likelihood estimator of the parameter, and where the updated estimator is computed without the historical observations in the sequential framework. For both the retrospective and the sequential detection, the test statistics converge to some distributions obtained from the standard Brownian motion under the null hypothesis of no change and diverge to infinity under the alternative; that is, these procedures are consistent. Some results of simulations as well as real data application are provided.
- Subjects :
- Statistics and Probability
Series (mathematics)
05 social sciences
Estimator
Mathematics - Statistics Theory
Statistics Theory (math.ST)
Poisson distribution
Conditional expectation
01 natural sciences
010104 statistics & probability
symbols.namesake
Wiener process
0502 economics and business
FOS: Mathematics
symbols
Applied mathematics
0101 mathematics
Statistics, Probability and Uncertainty
Change detection
050205 econometrics
Mathematics
Statistical hypothesis testing
Integer (computer science)
Subjects
Details
- ISSN :
- 1435926X and 00261335
- Volume :
- 85
- Database :
- OpenAIRE
- Journal :
- Metrika
- Accession number :
- edsair.doi.dedup.....580d1903dd11971a5449b52a6ffa298a