Öz
Kaynakça
- [1] I. Abramson, On bandwidth variation in kernel estimates—a square root law, Ann. Stat. 10:1217–1223, 1982.
Optimizing bandwidth parameter estimation for non-parametric regression using fixed-form threshold with Dmey and Coiflet wavelets
Öz
The variable bandwidth estimation process used in the Nadaraya-Watson kernel non-parametric regression was developed in this study through an algorithm based on a Fixed-Form threshold level value with wavelets (Dmey or Demyer and Coiflets). The process also involves reducing the effect of data noise by using a soft rule threshold or in the state that the data has long-tailed and multi-modal distributions. The Fixed-Form threshold level value estimates the bandwidth instead of the classical method (Geometric, Arithmetic mean, Range, and Median). The proposed method was evaluated through a simulation study, comparing it with four other Nadaraya-Watson kernel estimators (traditional techniques), using a MATLAB language created especially for this purpose with actual data. The findings show that the suggested method outperforms classical methods for all cases of simulations and real data in accurately estimating the bandwidth parameter of the non-parametric regression kernel function based on the Mean Square Error criterion.
Anahtar Kelimeler
Non-parametric regression kernel estimator bandwidth parameter fixed-form threshold wavelets
Kaynakça
- [1] I. Abramson, On bandwidth variation in kernel estimates—a square root law, Ann. Stat. 10:1217–1223, 1982.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Uygulamalı İstatistik |
| Bölüm | İstatistik |
| Yazarlar | |
| Erken Görünüm Tarihi | 27 Nisan 2025 |
| Yayımlanma Tarihi | |
| Gönderilme Tarihi | 25 Aralık 2024 |
| Kabul Tarihi | 24 Nisan 2025 |
| Yayımlandığı Sayı | Yıl 2025 Erken Görünüm |