Least informative distributions in maximum q-log-likelihood estimation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F18%3A00322787" target="_blank" >RIV/68407700:21340/18:00322787 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0378437118307283" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0378437118307283</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.physa.2018.06.004" target="_blank" >10.1016/j.physa.2018.06.004</a>

Result language
angličtina
Original language name
Least informative distributions in maximum q-log-likelihood estimation
Original language description
We use the maximum q-log-likelihood estimation for Least informative distributions (LIDs) in order to estimate the parameters in probability density functions (PDFs) efficiently and robustly when data include outlier(s). LIDs are derived by using convex combinations of two PDFs. A convex combination of two PDFs is composed of an underlying distribution and a contamination. The optimal criterion is obtained by minimizing the change of maximum q-log-likelihood function when the data contain small amount of contamination. In this paper, we make a comparison between ordinary maximum likelihood estimation, maximum q-log-likelihood estimation (MqLE) and LIDs based on MqLE for parameter estimation from data with outliers. We derive a new Fisher information matrix based on the score function for LID from M-function and use it for choice of optimal estimator in the class of MqLE. The model selection is done by the robust information criteria. We test the methods on the real data with outliers and estimate shape and scale parameters of probability distributions. As a result, we show that the LIDs based on MqLE provide the most robust and efficient estimation of the model parameters.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability

Project
<a href="/en/project/GF17-33812L" target="_blank" >GF17-33812L: An information-theoretical perspective on complex systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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