• Corpus ID: 88511939

A general model of regression using iterative series

@article{Sinha2011AGM,
  title={A general model of regression using iterative series},
  author={Nilotpal Kanti Sinha},
  journal={arXiv: Numerical Analysis},
  year={2011}
}
  • N. Sinha
  • Published 4 October 2011
  • Mathematics
  • arXiv: Numerical Analysis
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. 

References

SHOWING 1-5 OF 5 REFERENCES

Mathematical algorithms for linear regression

Linear L regression robust regression (ROBUST) ridge regression (RRL2, RRL1, RRL1) linear L regression with linear constraints linear L regression with non-negative parameters orthogonal linear L

Numerical methods for least square problems

TLDR
Theorems and statistical properties of least squares solutions are explained and basic numerical methods for solving least squares problems are described.

Nonlinear Regression with R

TLDR
This document summarizes the main findings from the second half of a two-day conference on model diagnostics and uncertainty, hypothesis testing and model selection of nls, the next generation of models for model selection and inference.

Iterative methods for optimization

  • C. Kelley
  • Computer Science
    Frontiers in applied mathematics
  • 1999
TLDR
Iterative Methods for Optimization does more than cover traditional gradient-based optimization: it is the first book to treat sampling methods, including the Hooke& Jeeves, implicit filtering, MDS, and Nelder& Mead schemes in a unified way.