:: Volume 27, Issue 2 (3-2023) ::
Andishe 2023, 27(2): 53-59 Back to browse issues page
Using the generalized maximum Tsallis entropy to estimate the ridge regression parameter
Manije Sanei tabass *
University of Sistan and Baluchestan
Abstract:   (308 Views)
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.
Keywords: Ridge regression, Generalized maximum entropy, Tsallis entropy, Generalized maximum Tsallis entropy
Full-Text [PDF 186 kb]   (236 Downloads)    
Type of Study: Research | Subject: Special
Received: 2023/02/20 | Accepted: 2023/05/19 | Published: 2023/05/19

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Volume 27, Issue 2 (3-2023) Back to browse issues page