Abstract
The optimal bandwidth selection in kernel-based nonparametric density estimation is one of the important parts in the spectral density estimation under long-range dependence (LRD). To improve the performance of the nonparametric spectral density estimation (NPSDE) under LRD, we propose a new cosine-based variable bandwidth selection method, which is motivated by variable bandwidth selection for density estimation and spectral density for autoregressive fractionally-integrated moving average models. The performance of the proposed method was illustrated through the simulation studies and data examples. The proposed cosine-based variable bandwidth selection method for NPSDE under LRD provides better performance than any other bandwidth selection method. Our method is robust to any values of the fractional differencing parameters.
| Original language | English |
|---|---|
| Pages (from-to) | 1158-1174 |
| Number of pages | 17 |
| Journal | Journal of Statistical Computation and Simulation |
| Volume | 92 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2022 |
Bibliographical note
Funding Information:This work was supported by the Korea Institute of Energy Technology Evaluation and Planning [grant number No. 20204010600060] National Research Foundation of Korea [grant number NRF-2019R1F1A1061691].
Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modelling and Simulation
- Statistics, Probability and Uncertainty
- Applied Mathematics