Optimizing bandwidth parameter estimation for non-parametric regression using fixed-form threshold with Dmey and Coiflet wavelets

Delshad Botani; Nazeera Kareem; Taha Ali; Bekhal Sedeeq

Makale detayları

Optimizing bandwidth parameter estimation for non-parametric regression using fixed-form threshold with Dmey and Coiflet wavelets

Yazarlar:

Delshad Botani | Nazeera Kareem | Taha Ali | Bekhal Sedeeq

Yayıncı:

Hacettepe University

Ana Başlıklar:

Applied Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 3, pp.1094-1106

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351220

pISSN:

2651-477X

Anahtar kelimeler:

Özet:

 The article aims to reduce the effect of data noise or outliers and estimate the optimal bandwidth parameter used in nonparametric regression models using a proposed method based on wavelet analysis, specifically Dmey and Coiflet wavelets with fixed-form threshold and apply the soft threshold, particularly when the data have long-tailed and multimodal distributions abnormal distribution. The fixed-form threshold level value estimates the bandwidth instead of the classical method geometric, arithmetic mean, range, and median. A simulation study was used to examine the suggested method, comparing it with four other Nadaraya-Watson kernel estimators classical 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.

 The article aims to reduce the effect of data noise or outliers and estimate the optimal bandwidth parameter used in nonparametric regression models using a proposed method based on wavelet analysis, specifically Dmey and Coiflet wavelets with fixed-form threshold and apply the soft threshold, particularly when the data have long-tailed and multimodal distributions abnormal distribution. The fixed-form threshold level value estimates the bandwidth instead of the classical method geometric, arithmetic mean, range, and median. A simulation study was used to examine the suggested method, comparing it with four other Nadaraya-Watson kernel estimators classical 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.

 The article aims to reduce the effect of data noise or outliers and estimate the optimal bandwidth parameter used in nonparametric regression models using a proposed method based on wavelet analysis, specifically Dmey and Coiflet wavelets with fixed-form threshold and apply the soft threshold, particularly when the data have long-tailed and multimodal distributions abnormal distribution. The fixed-form threshold level value estimates the bandwidth instead of the classical method geometric, arithmetic mean, range, and median. A simulation study was used to examine the suggested method, comparing it with four other Nadaraya-Watson kernel estimators classical 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.

 The article aims to reduce the effect of data noise or outliers and estimate the optimal bandwidth parameter used in nonparametric regression models using a proposed method based on wavelet analysis, specifically Dmey and Coiflet wavelets with fixed-form threshold and apply the soft threshold, particularly when the data have long-tailed and multimodal distributions abnormal distribution. The fixed-form threshold level value estimates the bandwidth instead of the classical method geometric, arithmetic mean, range, and median. A simulation study was used to examine the suggested method, comparing it with four other Nadaraya-Watson kernel estimators classical 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.

Makale detayları

Optimizing bandwidth parameter estimation for non-parametric regression using fixed-form threshold with Dmey and Coiflet wavelets

Yazarlar:

Delshad Botani | Nazeera Kareem | Taha Ali | Bekhal Sedeeq

Yayıncı:

Hacettepe University

Ana Başlıklar:

Applied Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 3, pp.1094-1106

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351220

pISSN:

2651-477X

Non-parametric regression

kernel estimator

bandwidth parameter

fixed-form threshold

wavelets

Sayı

Makale detayları

Optimizing bandwidth parameter estimation for non-parametric regression using fixed-form threshold with Dmey and Coiflet wavelets

Yazarlar: Delshad Botani | Nazeera Kareem | Taha Ali | Bekhal Sedeeq

Yayıncı: Hacettepe University

Ana Başlıklar: Applied Statistics

Dergi: Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 3, pp.1094-1106

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001351220

pISSN: 2651-477X

Non-parametric regression

kernel estimator

bandwidth parameter

fixed-form threshold

wavelets

Sayı

Yazarlar:

Delshad Botani | Nazeera Kareem | Taha Ali | Bekhal Sedeeq

Yayıncı:

Hacettepe University

Ana Başlıklar:

Applied Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 3, pp.1094-1106

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351220

pISSN:

2651-477X