Generalized Maximum Entropy Estimation; Application to Seed Rice Data in Sierra Leone

R.M. Bangura¹ , A.R Fofie² , M.Y. Turay³ , E.G. Kamara⁴

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.6 , pp.6-11, Dec-2020

Online published on Dec 31, 2020

Copyright © R.M. Bangura, A.R Fofie, M.Y. Turay, E.G. Kamara . 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.
 

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Abstract :
The maximum entropy method supports the subjective point of view with regards to probability, when drawing conclusions is plausible for incomplete information. The study of maximum entropy is of vital, when probabilities are given and we may want to measure the level of uncertainty of its outcome. Since matrix X is posed with problem of multicillinearity, we present an application of generalized maximum entropy procedure to seed rice data in Sierra Leone. We determined the estimation of three parameters based on generalized maximum entropy method and also provided a diagnostic fit for collinearity to obtain eigenvalues with their proportional variations. Analysis of variance is also presented to test the level of significance based on the GME procedure. With insufficient data, we also identified outliers with their corresponding observation points in the studentized residuals. Again, from the regression residual, a model was obtained, which shows the relationship between the regressors and the explained variable.

Key-Words / Index Term :
Generalized maximum entropy, Multicollinearity, Outliers, Residuals

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