A comparison between the two main components regression methods and the wavelet estimation method for the semi-parametric partial linear model
DOI:
https://doi.org/10.31272/jae.i138.1119Keywords:
semi-parametric, partial linear model, wavelet estimation method, regression of the main componentsAbstract
Many researchers have increased their interest in semi-parametric methods due to the diversity of data for the study community, where semi-parametric models provide a link between parametric and non-parametric models, as well as achieve estimates of a high level. It is the waveform estimation method (WAVE) and the main components regression method (PCR), where we use the simulation method and different sample sizes (128, 256, 512) and different variances (0.5, 1, 2) to compare the two methods and choose which is better, and through the simulation results and the graph, the researcher concluded that the best method is the wavelet estimation method (WAVE), which is where it overcame the principal components method (PCR) when the number of variables is four variables and eight variables and when using linear and quadratic smoothing functions.
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Hassan, A. H. (2017). Using difference technique in estimating the semiparametric regression model under the problem of near multicollinearity with practical application (Master’s thesis in Statistics). College of Administration and Economics, University of Baghdad.
Hamza, S. K. (2015). Wavelet analysis of regression model under missing data (Doctoral dissertation in Statistics). College of Administration and Economics, University of Baghdad.
Issa, A. M. (2011). Comparison of some semiparametric estimators for estimating the electricity consumption function of Baghdad city (Master’s thesis in Statistics). College of Administration and Economics, University of Baghdad.
Mohammed, M. K. (2014). Comparison of nonparametric and semiparametric models with missing values: Practical application on Iraq’s GDP for the period (1971–2010) (Master’s thesis in Statistics). College of Administration and Economics, University of Baghdad.
Hassan, Y. A. (2020). Comparison of wavelet and kernel estimators in estimating semiparametric regression models (partial linear models) with practical application (Master’s thesis in Statistics). College of Administration and Economics, University of Baghdad.
6- A. K. Louis ,P.Maab and A. Rieder , ( 1997 )" WaveletsTheory and Applications"
7- Chunling Liu, Shuang Guo and Chuanhua Wei , (2016 ) , " Principal components regression estimator of the parameters in partially linear models "
8 . Daubechies I., 1988. "Orthonormal Bases of Compactly Supported Wavelets". Communications on Pure and Applied Mathematics, Vol. XLI 909-996.
9 . Nason G.P., 1996. "Wavelet shrinkage using cross-validation". J. Roy. Statist. Soc. Ser. B 58, 463–479.
10 . Nason G.P., 2008. "Wavelet Methods in Statistics with R". Springer Science +Business Media, LLC
11. Speckman, P., 1988. "Kernel smoothing in partial linear models". J. Roy. Statist. Soc. Ser. B 50, 413–436.
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