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Showing 2 results for Logit
Shirin Shahsanam, Masoud Yarmohammadi,
Volume 20, Issue 2 (10-2015)
Abstract
Nowadays factor analysis has been extended greatly, and one of its applications is to analysis the attributes which are
not measurable directly. While the response variable has a Bernoulli distribution, using factor analysis method for
continuous quantities leads to invalid and misleading results. Factor analysis for Bernoulli response variable base on
Logit model is developed in this paper and its applications have been explained for read data from a research project
about the high school mathematics textbooks in Iran.
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.
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