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- W3048996507 abstract "The logistic regression model is one of the modern statistical methods developed to predict the set of quantitative variables (nominal or monotonous), and it is considered as an alternative test for the simple and multiple linear regression equation as well as it is subject to the model concepts in terms of the possibility of testing the effect of the overall pattern of the group of independent variables on the dependent variable and in terms of its use For concepts of standard matching criteria, and in some cases there is a correlation between the explanatory variables which leads to contrast variation and this problem is called the problem of Multicollinearity. In this study a generalized Liu estimator was introduced to combat the multicollinearity in the logistic regression model. The generalized Liu coefficient (shrinkage coefficient) was estimated in different ways and a comparison of these methods was performed using the mean square error criterion by applying to Monte Carlo simulation data and compared of road performance. Simulation results demonstrate that shrinkage parameter selection based on the work by Kibria (2003) ie (D5) is more efficient than methods." @default.
- W3048996507 created "2020-08-21" @default.
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- W3048996507 date "2020-07-01" @default.
- W3048996507 modified "2023-10-16" @default.
- W3048996507 title "Estimating Shrinkage Parameter of Generalized Liu Estimator in Logistic Regression Model" @default.
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- W3048996507 doi "https://doi.org/10.1088/1742-6596/1591/1/012034" @default.
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