Entropy 2014, 16(2), 675-698; doi:10.3390/e16020675
Article

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

1,2,3,* email and 1email
Received: 17 October 2013; in revised form: 17 December 2013 / Accepted: 14 January 2014 / Published: 23 January 2014
(This article belongs to the Special Issue Advances in Information Theory)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: 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.
Keywords: randomized data models; robustness; entropy function and entropy functional; entropy functional variation; likelihood function and likelihood functional; Volterra polynomials; multiplicative algorithms; symbolic computing
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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.

AMA Style

Popkov Y, Popkov A. New Methods of Entropy-Robust Estimation for Randomized Models under Limited Data. Entropy. 2014; 16(2):675-698.

Chicago/Turabian Style

Popkov, Yuri; Popkov, Alexey. 2014. "New Methods of Entropy-Robust Estimation for Randomized Models under Limited Data." Entropy 16, no. 2: 675-698.

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