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jeasiq-516
مقارنة بعض الطرائق الجزائية في تحليل انموذج المؤشر الواحد شبه المعلمي مع تطبيق عملي
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ABSTRACT

In this research been to use some of the semi-parametric methods the based on the different function penalty as well as the methods proposed by the researcher  because these methods work to estimate and variable selection of significant at once for single index model including (SCAD-NPLS method , the first proposal SCAD-MAVE method , the second proposal  ALASSO-MAVE method ) .As it has been using a method simulation time to compare between the semi-parametric estimation method studied , and various simulation experiments to identify the best method based on the comparison criteria (mean squares error(MSE) and average  mean squares error (AMSE)).

And the use of real data again to verify the performance of semi-parametric methods indeed the practical , was reached the best method for

estimate and variable selection of semi parametric single index model is the second method proposed (ALASSO-MAVE) for each of the simulation experiments of the first semi -parametric single index model and real data .

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Publication Date
Fri Aug 01 2014
Journal Name
Journal Of Economics And Administrative Sciences
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Publication Date
Thu Jun 29 2023
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Because the Coronavirus epidemic spread in Iraq, the COVID-19 epidemic of people quarantined due to infection is our application in this work. The numerical simulation methods used in this research are more suitable than other analytical and numerical methods because they solve random systems. Since the Covid-19 epidemic system has random variables coefficients, these methods are used. Suitable numerical simulation methods have been applied to solve the COVID-19 epidemic model in Iraq. The analytical results of the Variation iteration method (VIM) are executed to compare the results. One numerical method which is the Finite difference method (FD) has been used to solve the Coronavirus model and for comparison purposes. The numerical simulat

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Publication Date
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Publication Date
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Publication Date
Wed Mar 01 2023
Journal Name
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