"Compared some of the semi-parametric methods in analysis of single index model | ||
| journal of Economics And Administrative Sciences/ University of baghdad | ||
| Article 1, Volume 22, Issue 91, July 2016, Pages 367-388 | ||
| Abstract | ||
| As the process of estimate for model and variable selection significant is a crucial process in the semi-parametric modeling At the beginning of the modeling process often At there are many explanatory variables to Avoid the loss of any explanatory elements may be important as a result , the selection of significant variables become necessary , so the process of variable selection is not intended to simplifying model complexity explanation , and also predicting. In this research was to use some of the semi-parametric methods (LASSO-MAVE , MAVE and The proposal method (Adaptive LASSO-MAVE) for variable selection and estimate semi-parametric single index model (SSIM) at the same time . The result that the best method for estimating and the variable selection of semi parametric single index model is proposal method (Adaptive LASSO-MAVE) of first model and (LASSO-MAVE) of second method useful for average mean squares error (AMSE). | ||
| Keywords | ||
| single index model; MAVE; LASSO; Adaptive LASSO; variable selection | ||
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