Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/15003
Title: | Regularization with maximum entropy and quantum electrodynamics: the MERG(E) estimators |
Author: | Macedo, Pedro Scotto, Manuel Silva, Elvira |
Keywords: | Collinearity Linear regression Maximum entropy Micronumerosity Outliers Quantum electrodynamics |
Issue Date: | 2016 |
Publisher: | Taylor & Francis |
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. |
Peer review: | yes |
URI: | http://hdl.handle.net/10773/15003 |
DOI: | 10.1080/03610918.2014.957838 |
ISSN: | 0361-0918 |
Appears in Collections: | CIDMA - Artigos |
Files in This Item:
File | Description | Size | Format | |
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PaperCIS-SC2016.pdf | Paper in CIS-SC 2016 | 366.01 kB | Adobe PDF | View/Open |
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