Abstract
The work describes a series of techniques designed to obtain regression models resistant to multicollinearity and having some other features needed for meaningful results. These models include enhanced ridge-regressions with several regularization parameters, regressions by data segments and by levels of the dependent variable, latent class models, unitary response, models, orthogonal and equidistant regressions, minimization in Lp-metric, and other criteria and models. All the approaches have been practically implemented in various projects and found useful for decision making in economics, management, marketing research, and other fields requiring data modeling and analysis.
Subject
Applied Mathematics,Modeling and Simulation,Statistics and Probability
Reference26 articles.
1. Ridge regression: biased estimation for nonorthogonal problems;Hoerl;Technometrics,1970
2. Ridge regression: biased estimation for nonorthogonal problems;Hoerl;Technometrics,2000
3. The method of multifactor quasi-orthogonal regression;Lipovetsky;Industrial Laboratory, Plenum Publishing,1975
4. Statistical estimations of a single interrelation equation with measurement errors;Lipovetsky;Industrial Laboratory, Plenum Publishing,1976
5. Regression models of implicit functions;Lipovetsky;Industrial Laboratory, Plenum Publishing,1979
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