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A comparison of generalised maximum entropy and ordinary least square

A comparison of generalised maximum entropy and ordinary least square The generalised maximum entropy (GME) estimation method is based on the classic maximum entropy approach of Jaynes (1957). It has the ability to estimate the parameters of a regression model without imposing any constraints on the probability distribution of errors and it is robust even when we have ill-posed problems. In this paper, we simulate two sets of data from regression model with different distribution for disturbance, standard normal and Cauchy distributions respectively. For this dataset, regression coefficients are obtained by GME and OLS methods and these techniques are compared with each other for some sample sizes. Moreover, we have used some prior information on parameters to obtain GME estimators. The estimation results of GME in the case of non-normal distributed are discussed here. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Information and Decision Sciences Inderscience Publishers

A comparison of generalised maximum entropy and ordinary least square

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1756-7017
eISSN
1756-7025
DOI
10.1504/IJIDS.2018.095495
Publisher site
See Article on Publisher Site

Abstract

The generalised maximum entropy (GME) estimation method is based on the classic maximum entropy approach of Jaynes (1957). It has the ability to estimate the parameters of a regression model without imposing any constraints on the probability distribution of errors and it is robust even when we have ill-posed problems. In this paper, we simulate two sets of data from regression model with different distribution for disturbance, standard normal and Cauchy distributions respectively. For this dataset, regression coefficients are obtained by GME and OLS methods and these techniques are compared with each other for some sample sizes. Moreover, we have used some prior information on parameters to obtain GME estimators. The estimation results of GME in the case of non-normal distributed are discussed here.

Journal

International Journal of Information and Decision SciencesInderscience Publishers

Published: Jan 1, 2018

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