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.
|Number of pages||17|
|Journal||Journal of Statistical Computation and Simulation|
|Publication status||Published - 2022|
Bibliographical noteFunding 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].
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modelling and Simulation
- Statistics, Probability and Uncertainty
- Applied Mathematics