Title : ( The Generalized Maximum Tsallis Entropy Estimators and Applications to the Portland Cement Dataset )
Authors: manije sanei , Gholam Reza Mohtashami Borzadaran ,
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Abstract
Tsallis entropy is a generalized form of entropy and tends to Shannon entropy when q → 1. Using Tsallis entropy, an alternative estimation methodology(Generalized Maximum Tsallis Entropy) is introduced and used to estimate the parameters in a linear regression model when the basic data are ill-conditioned. We describe the Generalized Maximum Tsallis Entropy and for q = 2 we call that GMET2 estimates. We apply The GMET2 estimates for estimating the linear regression model Y = Xβ + e that the design matrix X is subject to severe multicollinearity. We compared the GMET2, GME, OLS and IRLS estimators on the analyzed dataset on Portland cement.
Keywords
, Generalized Maximum Tsallis Entropy, Least square estimator, Linear regression model, Multicollinearity , Support points.
@article{paperid:1056666,
author = {Sanei, Manije and Mohtashami Borzadaran, Gholam Reza},
title = {The Generalized Maximum Tsallis Entropy Estimators and Applications to the Portland Cement Dataset},
journal = {Communications in Statistics Part B: Simulation and Computation},
year = {2016},
volume = {46},
number = {4},
month = {January},
issn = {0361-0918},
pages = {3284--3293},
numpages = {9},
keywords = {Generalized Maximum Tsallis Entropy; Least square estimator; Linear regression
model; Multicollinearity ;Support points.},
}
%0 Journal Article
%T The Generalized Maximum Tsallis Entropy Estimators and Applications to the Portland Cement Dataset
%A Sanei, Manije
%A Mohtashami Borzadaran, Gholam Reza
%J Communications in Statistics Part B: Simulation and Computation
%@ 0361-0918
%D 2016
