Regularization with maximum entropy and quantum electrodynamics: the MERG(E) estimators

• Autor(a) principal: Macedo, Pedro
• Data de Publicação: 2016
• Outros Autores: Scotto, Manuel, Silva, Elvira
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/15003
• topic: Collinearity; Linear regression; Maximum entropy; Micronumerosity; Outliers; Quantum electrodynamics

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.