A New Extension of Thinning-Based Integer-Valued Autoregressive Models for Count Data.
Entropy (Basel)
; 23(1)2020 Dec 31.
Article
en En
| MEDLINE
| ID: mdl-33396549
The thinning operators play an important role in the analysis of integer-valued autoregressive models, and the most widely used is the binomial thinning. Inspired by the theory about extended Pascal triangles, a new thinning operator named extended binomial is introduced, which is a general case of the binomial thinning. Compared to the binomial thinning operator, the extended binomial thinning operator has two parameters and is more flexible in modeling. Based on the proposed operator, a new integer-valued autoregressive model is introduced, which can accurately and flexibly capture the dispersed features of counting time series. Two-step conditional least squares (CLS) estimation is investigated for the innovation-free case and the conditional maximum likelihood estimation is also discussed. We have also obtained the asymptotic property of the two-step CLS estimator. Finally, three overdispersed or underdispersed real data sets are considered to illustrate a superior performance of the proposed model.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Entropy (Basel)
Año:
2020
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Suiza