A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

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Background and Objectives: Logistic regression is one of the most widely used generalized linear models for analysis of the relationships between one or more explanatory variables and a categorical response. Strong correlations among explanatory variables (multicollinearity) reduce the efficiency of model to a considerable degree. In this study we used latent variables to reduce the effects of ...

Using Classic Discriminant Analysis and Detection Function for Separation of Chemical Victims in Sardasht City to Exposed and Non Exposure Mustard Groups in 2013 and Comparison with Logistic Regression

Selecting a Two-Group Classification Weighting Algorithm: Take Two

The two-group cross-validation classification accuracies of six algorithms (i.e., least squares, ridge regression, principal components, a common factor method, equal weighting, and logistic regression) were compared as a function of degree of validity concentration, group separation, and number of subjects. Therein, the findings of two previous studies were extended to the latter three methods...

Robust Estimation in Linear Regression with Molticollinearity and Sparse Models

‎One of the factors affecting the statistical analysis of the data is the presence of outliers‎. ‎The methods which are not affected by the outliers are called robust methods‎. ‎Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers‎. ‎Besides outliers‎, ‎the linear dependency of regressor variables‎, ‎which is called multicollinearity...

Liu-Type Logistic Estimators with Optimal Shrinkage Parameter

Multicollinearity in logistic regression affects the variance of the maximum likelihood estimator negatively. In this study, Liu-type estimators are used to reduce the variance and overcome the multicollinearity by applying some existing ridge regression estimators to the case of logistic regression model. A Monte Carlo simulation is given to evaluate the performances of these estimators when t...