Comparison between Several Regression Models

Xiao Cui Yin

doi:10.4028/www.scientific.net/AMM.530-531.601

Paper Titles

Direction of Arrival Estimation Based on a Tensor Approach
p.581

Complexity Analysis of Rainfall in Jinsha River Basin, China
p.586

Measurements and Mathematical Characterization of Uncertain Information
p.591

Parameter Estimation of Rayleigh Distribution Based on MCEM Algorithm
p.597

Comparison between Several Regression Models
p.601

Estimation of SV Model with Leverage Effect Based on MCMC Technique
p.605

Existence of Periodic Solution for a Class of Symmetric Superquadratic Second-Order Non-Autonomous Hamiltonian Systems
p.609

The Series Solution of Fractional Partial Differential Equations via Homotopy Analysis Method
p.613

On Semilattices of R-Ample Semigroups with Left Central Idempotents
p.617

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 530-531Comparison between Several Regression Models

Comparison between Several Regression Models

Abstract:

This paper study ordinary linear regression and ridge regression, ridge regression includes ordinary ridge regression (ORR) and generalized ridge regression (GRR). Comparison between these methods are made by an example, the results show that ridge regression has smaller standard deviation and MSE than OLS, and among all the methods, GRR is better than others.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 530-531)

Pages:

601-604

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.530-531.601

Citation:

Cite this paper

Online since:

February 2014

Authors:

Xiao Cui Yin*

Keywords:

Generalized Ridge Regression, Multicollinearity, Ordinary Ridge Regression

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] M. El-Dereny and N. I. Rashwan: Solving Multicollinearity Problem Using Ridge Regression Models, Int. J. Contemp. Math. Sciences, Vol. 6, 2011, No. 12, p.585 – 600.

Google Scholar

[2] W.H. Green: Econometric Analysis, Mac-Millan, New York, (1993).

Google Scholar

[3] A.E. Hoerl, and R.W. Kennard, Ridge Regression: Biased Estimation for nonorthogonal Problems, Technometrics, Vol. 42, 2000, p.80 – 86.

DOI: 10.1080/00401706.2000.10485983

Google Scholar

[4] T.M. John, Mean Squared Error Properties of the Ridge Regression Estimated Linear Probability Models, Ph.D. Dissertation, University of Delaware, (1998).

Google Scholar

[5] H. L. Song: Comparison of Regression Techniques for certain patterned Matrices in the errors in variables Model, Ph.D. Dissertation, American University, Washington, (2001).

Google Scholar

[6] H.D. Vinod, and A. Ullah: Recent Advances in Regression Models, Marcel Dekker, New York, (1981).

Google Scholar

[7] A.E. Hoerl, R.W. Kannarda, K.F. Baldwinb: Ridge regression: Some simulations. Commun. Stat., Vol 4, 1975, pp.105-123.

Google Scholar

[8] A. E. Hoerl and R. Kennard, Ridge Regression: Biased Estimation for Non orthogonal Problems, Technometrics, vol. 12, 1970, p.55 – 67. Zbl 0202. 17205.

DOI: 10.1080/00401706.1970.10488634

Google Scholar