A New Perturbation Approach to Optimal Polynomial Regression
AbstractA new approach to polynomial regression is presented using the concepts of orders of magnitudes of perturbations. The data set is normalized with the maximum values of the data first. The polynomial regression of arbitrary order is then applied to the normalized data. Theorems for special properties of the regression coefficients as well as some criteria for determining the optimum degrees of the regression polynomials are posed and proven. The new approach is numerically tested, and the criteria for determining the best degree of the polynomial for regression are discussed. View Full-Text
This paper was processed and accepted under the editorial system of the ASR before its transfer to MDPI.
Share & Cite This Article
Pakdemirli, M. A New Perturbation Approach to Optimal Polynomial Regression. Math. Comput. Appl. 2016, 21, 1.
Pakdemirli M. A New Perturbation Approach to Optimal Polynomial Regression. Mathematical and Computational Applications. 2016; 21(1):1.Chicago/Turabian Style
Pakdemirli, Mehmet. 2016. "A New Perturbation Approach to Optimal Polynomial Regression." Math. Comput. Appl. 21, no. 1: 1.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.