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Showing 2 results for Generalized Maximum Entropy
Mahsa Markani, Manije Sanei Tabas, Habib Naderi, Hamed Ahmadzadeh, Javad Jamalzadeh,
Volume 26, Issue 2 (3-2022)
Abstract
When working on a set of regression data, the situation arises that this data
It limits us, in other words, the data does not meet a set of requirements. The generalized entropy method is able to estimate the model parameters Regression is without applying any conditions on the error probability distribution. This method even in cases where the problem Too poorly designed (for example when sample size is too small, or data that has alignment
They are high and ...) is also capable. Therefore, the purpose of this study is to estimate the parameters of the logistic regression model using the generalized entropy of the maximum. A random sample of bank customers was collected and in this study, statistical work and were performed to estimate the model parameters from the binary logistic regression model using two methods maximum generalized entropy (GME) and maximum likelihood (ML). Finally, two methods were performed. We compare the mentioned. Based on the accuracy of MSE criteria to predict customer demand for long-term account opening obtained from logistic regression using both GME and ML methods, the GME method was finally more accurate than the ml method.
Dr Manije Sanei Tabass,
Volume 27, Issue 2 (3-2023)
Abstract
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.
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