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Estimate Kernel Ridge Regression Function in Multiple Regression | ||
| journal of Economics And Administrative Sciences/ University of baghdad | ||
| Article 1, Volume 24, Issue 103, 2018, Pages 411-419 | ||
| Abstract | ||
| In general, researchers and statisticians in particular have been usually used non-parametric regression models when the parametric methods failed to fulfillment their aim to analyze the models precisely. In this case the parametic methods are useless so they turn to non-parametric methods for its easiness in programming. Non-parametric methods can also used to assume the parametric regression model for subsequent use. Moreover, as an advantage of using non-parametric methods is to solve the problem of Multi-Colinearity between explanatory variables combined with nonlinear data. This problem can be solved by using kernel ridge regression which depend on what so-called bandwidth estimation (smoothing parameters). Therefore, for this purpose two different methods were used to estimate the smoothing parameter (Maximum Likelihood Cross-Validation (MLCV) and Akaike Information Criterion (AIC)). Furthermore, a comparision between the previouse methods had been provided using simulation technique , and the method of Akaike Information Criterion (AIC) has been found to be the best for the Gaussian function . | ||
| Keywords | ||
| kernel ridge regression KRR; MLCV; AIC; Regularization parameter; KRR | ||
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