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Effects of Overdispersion on Testing for Serial Dependence in the Time Series of Counts DataEffects of Overdispersion on Testing for Serial Dependence in the Time Series of Counts Data

Other Titles
Effects of Overdispersion on Testing for Serial Dependence in the Time Series of Counts Data
Authors
김희영박유성
Issue Date
2010
Publisher
한국통계학회
Keywords
Overdispersion; negative binomial; generalized Poisson; time series of counts data; serial dependence
Citation
Communications for Statistical Applications and Methods, v.17, no.6, pp.829 - 843
Indexed
KCI
Journal Title
Communications for Statistical Applications and Methods
Volume
17
Number
6
Start Page
829
End Page
843
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/117396
ISSN
2287-7843
Abstract
To test for the serial dependence in time series of counts data, (2003) evaluated the size and power of several tests under the class of INARMA models based on binomial thinning operations for Poisson marginal distributions. The overdispersion phenomenon(i.e., a variance greater than the expectation) is common in the real world. Overdispersed count data can be modeled by using alternative thinning operations such as random coefficient thinning,iterated thinning, and quasi-binomial thinning. Such thinning operations can lead to time series models of counts with negative binomial or generalized Poisson marginal distributions. This paper examines whether the test statistics used by (2003) on serial dependence in time series of counts data are affected by overdispersion.
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