A general model of regression using iterative series
Abstract
We present a new and general method of weighted least square univariate regression where the dependent variable is expanded as a series of suitably chosen functions of the independent variables. Each term of the series is obtained by an iterative process which reduces the sum of the square of the residuals. Thus by evaluating the regression series to a sufficiently large number of terms we can, in principle, reduce the sum of the square of residuals and improve the accuracy of the fit.
 Publication:

arXiv eprints
 Pub Date:
 October 2011
 arXiv:
 arXiv:1110.0811
 Bibcode:
 2011arXiv1110.0811K
 Keywords:

 Mathematics  Numerical Analysis;
 Mathematics  Statistics Theory;
 93E24;
 62J05
 EPrint:
 Need major changes and corrections