Parametric Refining in Data Processing

This paper gives a method for getting the values of the unknown parameters of the semi-parametric model under the principle of penalized least squares with a positive definite regularity matrix. Based on the statistic characteristic of random errors, the mathematical expectation, variance and mean square error of the parameters estimator getting from this method are discussed in detail. The difference between the parameters values that were given by semi-parametric models and by general least squares is compared. It is shown clearly by the theoretical analysis and the simulating computation that the method of semi-parametric adjustment is better than that of least squares if the smoothing parameter takes a suitable value. Our study shows how to choose a reasonable value of the smoothing parameter and itpsilas influence to the precision of the mathematic model is also given.