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» Symbolic regression in multicollinearity problems
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GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
13 years 10 months ago
Symbolic regression in multicollinearity problems
In this paper the potential of GP-generated symbolic regression for alleviating multicollinearity problems in multiple regression is presented with a case study in an industrial s...
Flor A. Castillo, Carlos M. Villa
CSDA
2006
304views more  CSDA 2006»
13 years 4 months ago
Using principal components for estimating logistic regression with high-dimensional multicollinear data
The logistic regression model is used to predict a binary response variable in terms of a set of explicative ones. The estimation of the model parameters is not too accurate and t...
Ana M. Aguilera, Manuel Escabias, Mariano J. Valde...
WSC
2004
13 years 6 months ago
Teaching Regression with Simulation
Computer simulations can be used to teach complicated statistical concepts in linear regression more quickly and effectively than traditional lecture alone. In introductory applie...
John H. Walker
EUROGP
2001
Springer
105views Optimization» more  EUROGP 2001»
13 years 9 months ago
Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems
Abstract. In this paper we continue our study on adaptive genetic programming. We use Stepwise Adaptation of Weights (saw) to boost performance of a genetic programming algorithm o...
Jeroen Eggermont, Jano I. van Hemert
CSDA
2007
128views more  CSDA 2007»
13 years 4 months ago
Regularized linear and kernel redundancy analysis
Redundancy analysis (RA) is a versatile technique used to predict multivariate criterion variables from multivariate predictor variables. The reduced-rank feature of RA captures r...
Yoshio Takane, Heungsun Hwang