RT - Journal Article
T1 - Using the generalized maximum Tsallis entropy to estimate the ridge regression parameter
JF - Andishe-_ye-Amari
YR - 2023
JO - Andishe-_ye-Amari
VO - 27
IS - 2
UR - http://andisheyeamari.irstat.ir/article-1-910-en.html
SP - 53
EP - 59
K1 - Ridge regression
K1 - Generalized maximum entropy
K1 - Tsallis entropy
K1 - Generalized maximum Tsallis entropy
AB - Regression analysis using the method of least squares requires the establishment of basic assumptions. One of the problems of regression analysis in this way faces major problems is the existence of collinearity among the regression variables. Many methods to solve the problems caused by the existence of the same have been introduced linearly. One of these methods is ridge regression. In this article, a new estimate for the ridge parameter using generalized maximum Tsallis entropy is presented and we call it the Ridge estimator of generalized maximum Tsallis entropy. For the cement dataset Portland, which have strong collinearity and since 1332, different estimators have been presented for these data, this estimator is calculated and We compare the generalized maximum Tsallis entropy ridge estimator, generalized maximum entropy ridge estimator and the least squares estimator.
LA eng
UL http://andisheyeamari.irstat.ir/article-1-910-en.html
M3
ER -