Additive Nonparametric Regression Model Using Smoothing Kernel Function

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Year:
2015
Type of Publication:
Article
Keywords:
Additive Model, Back Fitting, Kernel,Estimator, Nonparametric Regression, Nadaraya Watson,Smoothing
Authors:
Monem A. M.; Khalida A. S.
Journal:
IJISM
Volume:
3
Number:
1
Pages:
18-23
Month:
Jan.-Feb.
ISSN:
2347-9051
Abstract:
The important issue in the main applications of statistical represented by the distribution and the assumptions for the parent population (from which the sample is drawn) has a specific distribution characteristics to be represented community representation, but in many cases does not know the form of the basis distribution so we needs statistical techniques do not depend on the distribution or assumptions about the phenomenon required study (depend on the free distribution for the data), and these methods are the nonparametric regression methods that depend directly on the data when estimating equation. In this research was review some methods in nonparametric regression methods, like the (Additive regression model) with some methods for estimating the smoothing parameters (one of these methods have been proposed to find an initial value for the smoothing parameter with Kernel functions) , and then compared the results of the methods mentioned above, among themselves using tests and statistical standards following:(MSE, RMSE , R2 , R adj 2 , and F-statistic). The application is done for the real data (elements of climate), daily average temperatures for the period from (1/1/2011 to 30/6/2012) registered with the directorate of Meteorological and Seismology in Sulaimani with different samples sizes (182, 365, and 547).To achieve the objective of the study by using (XLSTAT- 2012) Software and a special personal program by Excel.

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