Next Article in Journal
Thermodynamics as Control Theory
Next Article in Special Issue
Prediction Method for Image Coding Quality Based on Differential Information Entropy
Previous Article in Journal
Dynamical Stability and Predictability of Football Players: The Study of One Match
Previous Article in Special Issue
Some Convex Functions Based Measures of Independence and Their Application to Strange Attractor Reconstruction
Article Menu

Export Article

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

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

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.
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)
View Full-Text   |   Download PDF [525 KB, uploaded 24 February 2015]   |  

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 randomized data models; robustness; entropy function and entropy functional; entropy functional variation; likelihood function and likelihood functional; Volterra polynomials; multiplicative algorithms; symbolic computing
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top