# Cite Article

### Wavelet Estimation of Semi-parametric Regression Model

Choose citation format## BibTeX

@article{IJASEIT12585, author = {Ahmed Shaker Mohammed Tahir and Firas Monther Jassim}, title = {Wavelet Estimation of Semi-parametric Regression Model}, journal = {International Journal on Advanced Science, Engineering and Information Technology}, volume = {10}, number = {4}, year = {2020}, pages = {1374--1379}, keywords = {partially linear model; wavelet estimate; speckman method; nadaraya-watson smoothing; local linear smoothing.}, abstract = {The semi-parametric regression model combines parametric and nonparametric regression. However, non-parametric estimation may provide flexible solutions to the problems suffers by the regression model, but the problem of dimensionality that this estimator suffers, which occurs due to the increasing number of explanatory variables, still remain, this, in turn, may reduce the accuracy of the estimation process. Estimate the non-parametric part of the semi-parametric models that can be studied using conventional non-parametric methods such as the Spline Smoothing and Kernel Smoothing. However, there are other non-parametric methods that can be used, therefore, in this paper, the semi-parametric regression model was estimated by employing the wavelet estimate for the soft threshold, according to the "Speckman" method, and then comparing it with the two methods, Nadaraya-Watson and Local Linear, through the implementation of simulation experiments that included different sample sizes and threshold values. The parametric part estimation of the partially linear model according to the least-squares method was not identical to those estimates using the Speckman method, that is because the least-squares method was not appropriate for the uneven nature of the number of weekly work hours. Simulation experiments have demonstrated the efficiency of the wavelet estimation method and its superiority over other methods. The above estimation methods were applied to real data related to the study of the production value for the public industrial sector in Iraq, and some factors affect it, such as the value of industrial supplies, the total wages of workers, and the number of workers.

}, issn = {2088-5334}, publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development}, url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12585}, doi = {10.18517/ijaseit.10.4.12585} }

## EndNote

%A Tahir, Ahmed Shaker Mohammed %A Jassim, Firas Monther %D 2020 %T Wavelet Estimation of Semi-parametric Regression Model %B 2020 %9 partially linear model; wavelet estimate; speckman method; nadaraya-watson smoothing; local linear smoothing. %! Wavelet Estimation of Semi-parametric Regression Model %K partially linear model; wavelet estimate; speckman method; nadaraya-watson smoothing; local linear smoothing. %XThe semi-parametric regression model combines parametric and nonparametric regression. However, non-parametric estimation may provide flexible solutions to the problems suffers by the regression model, but the problem of dimensionality that this estimator suffers, which occurs due to the increasing number of explanatory variables, still remain, this, in turn, may reduce the accuracy of the estimation process. Estimate the non-parametric part of the semi-parametric models that can be studied using conventional non-parametric methods such as the Spline Smoothing and Kernel Smoothing. However, there are other non-parametric methods that can be used, therefore, in this paper, the semi-parametric regression model was estimated by employing the wavelet estimate for the soft threshold, according to the "Speckman" method, and then comparing it with the two methods, Nadaraya-Watson and Local Linear, through the implementation of simulation experiments that included different sample sizes and threshold values. The parametric part estimation of the partially linear model according to the least-squares method was not identical to those estimates using the Speckman method, that is because the least-squares method was not appropriate for the uneven nature of the number of weekly work hours. Simulation experiments have demonstrated the efficiency of the wavelet estimation method and its superiority over other methods. The above estimation methods were applied to real data related to the study of the production value for the public industrial sector in Iraq, and some factors affect it, such as the value of industrial supplies, the total wages of workers, and the number of workers.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12585 %R doi:10.18517/ijaseit.10.4.12585 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 4 %@ 2088-5334

## IEEE

Ahmed Shaker Mohammed Tahir and Firas Monther Jassim,"Wavelet Estimation of Semi-parametric Regression Model,"International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 4, pp. 1374-1379, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.4.12585.

## RefMan/ProCite (RIS)

TY - JOUR AU - Tahir, Ahmed Shaker Mohammed AU - Jassim, Firas Monther PY - 2020 TI - Wavelet Estimation of Semi-parametric Regression Model JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 4 Y2 - 2020 SP - 1374 EP - 1379 SN - 2088-5334 PB - INSIGHT - Indonesian Society for Knowledge and Human Development KW - partially linear model; wavelet estimate; speckman method; nadaraya-watson smoothing; local linear smoothing. N2 -The semi-parametric regression model combines parametric and nonparametric regression. However, non-parametric estimation may provide flexible solutions to the problems suffers by the regression model, but the problem of dimensionality that this estimator suffers, which occurs due to the increasing number of explanatory variables, still remain, this, in turn, may reduce the accuracy of the estimation process. Estimate the non-parametric part of the semi-parametric models that can be studied using conventional non-parametric methods such as the Spline Smoothing and Kernel Smoothing. However, there are other non-parametric methods that can be used, therefore, in this paper, the semi-parametric regression model was estimated by employing the wavelet estimate for the soft threshold, according to the "Speckman" method, and then comparing it with the two methods, Nadaraya-Watson and Local Linear, through the implementation of simulation experiments that included different sample sizes and threshold values. The parametric part estimation of the partially linear model according to the least-squares method was not identical to those estimates using the Speckman method, that is because the least-squares method was not appropriate for the uneven nature of the number of weekly work hours. Simulation experiments have demonstrated the efficiency of the wavelet estimation method and its superiority over other methods. The above estimation methods were applied to real data related to the study of the production value for the public industrial sector in Iraq, and some factors affect it, such as the value of industrial supplies, the total wages of workers, and the number of workers.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12585 DO - 10.18517/ijaseit.10.4.12585

## RefWorks

RT Journal Article ID 12585 A1 Tahir, Ahmed Shaker Mohammed A1 Jassim, Firas Monther T1 Wavelet Estimation of Semi-parametric Regression Model JF International Journal on Advanced Science, Engineering and Information Technology VO 10 IS 4 YR 2020 SP 1374 OP 1379 SN 2088-5334 PB INSIGHT - Indonesian Society for Knowledge and Human Development K1 partially linear model; wavelet estimate; speckman method; nadaraya-watson smoothing; local linear smoothing. AB