Regularization with Maximum Entropy and Quantum Electrodynamics: The Merg(E) Estimators. Issue 3 (15th March 2016)

• Record Type: Journal Article
• Authors: Macedo, Pedro; Scotto, Manuel; Silva, Elvira
• Is Part Of: Communications in statistics. Volume 45:Issue 3(2016)
• Page Start: 1143
• Page End: 1157
• Publication Date: 2016-03-15
• Subjects: collinearity -- linear regression -- maximum entropy -- micronumerosity -- outliers -- quantum electrodynamics; 47A52 -- 62J05 -- 62J07 -- 81V10 -- 94A17; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2014.957838
• Languages: English
• ISSNs: 0361-0918

Abstract

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.