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Open AccessArticle

New Methods of Entropy-Robust Estimation for Randomized Models under Limited Data

by Yuri Popkov 1,2,3,* and Alexey Popkov 1
1
Institute for Systems Analysis of Russian Academy of Sciences, 9 prospect 60-let Octyabrya, Moscow 117312, Russia
2
Moscow Institute of Physics and Technology, 9 Institutskiy pereulok, g. Dolgoprudny, Moskovskaya oblast 141700, Russia
3
Higher School of Economics, 20 Myasnitskaya, Moscow 101000, Russia
*
Author to whom correspondence should be addressed.
Entropy 2014, 16(2), 675-698; https://doi.org/10.3390/e16020675
Received: 17 October 2013 / Revised: 17 December 2013 / Accepted: 14 January 2014 / Published: 23 January 2014
(This article belongs to the Special Issue Advances in Information Theory)
The paper presents a new approach to restoration characteristics randomized models under small amounts of input and output data. This approach proceeds from involving randomized static and dynamic models and estimating the probabilistic characteristics of their parameters. We consider static and dynamic models described by Volterra polynomials. The procedures of robust parametric and non-parametric estimation are constructed by exploiting the entropy concept based on the generalized informational Boltzmann’s and Fermi’s entropies. View Full-Text
Keywords: randomized data models; robustness; entropy function and entropy functional; entropy functional variation; likelihood function and likelihood functional; Volterra polynomials; multiplicative algorithms; symbolic computing randomized data models; robustness; entropy function and entropy functional; entropy functional variation; likelihood function and likelihood functional; Volterra polynomials; multiplicative algorithms; symbolic computing
MDPI and ACS Style

Popkov, Y.; Popkov, A. New Methods of Entropy-Robust Estimation for Randomized Models under Limited Data. Entropy 2014, 16, 675-698.

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