Regularization with Maximum Entropy and Quantum Electrodynamics: The Merg(E) Estimators

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Macedo, P (Author) Other; Scotto, M (Author) Other; Elvira Silva (Author) FEP

Journal

Title: Communications in Statistics Part B: Simulation and Computation

Vol. 45

Pages: 1143-1157

ISSN: 0361-0918

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-00K-BMV

DOI: 10.1080/03610918.2014.957838

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15