Regularization with Maximum Entropy and Quantum Electrodynamics: The Merg(E) Estimators |
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Authors: | Pedro Macedo Manuel Scotto Elvira Silva |
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Institution: | 1. Department of Mathematics, CIDMA - Center for Research and Development in Mathematics and Applications, University of Aveiro, Aveiro, Portugal;2. CEF.UP, Faculty of Economics, University of Porto Rua Dr. Roberto Frias, 4200-464 Porto, Portugal |
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Abstract: | It is well-known that under fairly conditions linear regression becomes a powerful statistical tool. In practice, however, some of these conditions are usually not satisfied and regression models become ill-posed, implying that the application of traditional estimation methods may lead to non-unique or highly unstable solutions. Addressing this issue, in this paper a new class of maximum entropy estimators suitable for dealing with ill-posed models, namely for the estimation of regression models with small samples sizes affected by collinearity and outliers, is introduced. The performance of the new estimators is illustrated through several simulation studies. |
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Keywords: | collinearity linear regression maximum entropy micronumerosity outliers quantum electrodynamics |
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