Statistical inference for time series with non-precise data.
- Title
- Statistical inference for time series with non-precise data.
- Author
- 정혜영
- Keywords
- Fuzzy time series; Fuzzy autoregressive model; Fuzzy least squares estimation; Normality; Consistency; Asymptotic relative efficiency
- Issue Date
- 2019-08
- Publisher
- Elsevier Inc.
- Citation
- International Journal of Approximate Reasoning, v. 114, Page. 99-114
- Abstract
- For linear time series models, the asymptotic properties of least squares estimation are known. An analogous result for the stationary time series with non-precise data is considered in this paper. The least squares estimation for the general autoregressive model with fuzzy data is investigated with a suitable fuzzy metric. The fuzzy-type least squares approach is defined, and analogues of the conventional normal equations and fuzzy least squares estimators (FLSEs) are also derived. A numerical example for computing FLSEs and forecasting the future values of fuzzy series is given with the financial data. Asymptotic normality and consistency are established for the FLSEs. A confidence region based on a class of FLSEs is constructed. The asymptotic relative efficiency of FLSEs with respect to the crisp least squares estimators is provided. Some simulation results are also presented to illustrate the small sample behavior of FLSEs.
- URI
- https://www.sciencedirect.com/science/article/pii/S0888613X18306248https://repository.hanyang.ac.kr/handle/20.500.11754/125309
- ISSN
- 0888-613X
- DOI
- 10.1016/j.ijar.2019.08.002
- Appears in Collections:
- COLLEGE OF SCIENCE AND CONVERGENCE TECHNOLOGY[E](과학기술융합대학) > APPLIED MATHEMATICS(응용수학과) > Articles
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