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Estimation of Regression Model Parameters with Specific Constraints

Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 54)

Abstract

Consider the regression
$${y}_{t} =\tilde{ f}({\mathbf{x}}_{t},{\alpha }^{0}) + {\epsilon }_{ t},\quad t = 1,2,\ldots,$$
(1.1)
where y t 1 is the dependent variable, x t q is an argument (regressor), α0 n is a true regression parameter (unknown), \(\tilde{f}({\mathbf{x}}_{t},\alpha )\) is some (nonlinear) function of α, ε t is a noise, and t is an observation number.

Keywords

Estimation Problem Regression Parameter Regression Function Inequality Constraint Full Rank 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Mathematical Methods of Operation Research V.M. Glushkov Institute of CyberneticsNational Academy of Science of UkraineKievUkraine
  2. 2.Department of Economical Cybernetics and Information TechnologyNational Mining UniversityDnepropetrovskUkraine

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