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

Entropy

Yuri Popkov¹ 

[1] Institute for Systems Analysis of Russian Academy of Sciences, 9 prospect 60-let Octyabrya, Moscow 117312, Russia; E-Mail:

关键词: randomized data models;    robustness;    entropy function and entropy functional;    entropy functional variation;    likelihood function and likelihood functional;    Volterra polynomials;    multiplicative algorithms;    symbolic computing;   

DOI  :  10.3390/e16020675

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland