The logistic regression model is used when the response variables are dichotomous. In the presence of multicollinearity, the variance of the maximum likelihood estimator (MLE) becomes inflated. The Liu estimator for the linear regression model is proposed by Liu to remedy this problem. Urgan and Tez and Mansson et al. examined the Liu estimator (LE) for the logistic regression model. We introduced the restricted Liu estimator (RLE) for the logistic regression model. Moreover, a Monte Carlo simulation study is conducted for comparing the performances of the MLE, restricted maximum likelihood estimator (RMLE), LE, and RLE for the logistic regression model. Copyright © Taylor & Francis Group, LLC
In innovation analysis the logit model used to be applied on available data when the dependent varia...
Multiple linear interferences are a fundamental obstacle in many standard models. This problem appea...
Multiple linear interferences are a fundamental obstacle in many standard models. This problem appea...
Multicollinearity problem in logistic regression causes an inflation in the variance of the Maximum ...
Multicollinearity in logistic regression affects the variance of the maximum likelihood estimator ne...
Multicollinearity negatively affects the efficiency of the maximum likelihood estimator (MLE) in bot...
Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper pr...
In this study, we introduce iterative restricted Liu estimator to combat multicollinearity in genera...
This paper deals with the problem of multicollinearity in a multiple linear regression model with li...
Multicollinearity among the explanatory variables seriously effects the maximum likelihood estimator...
Consider the regression model y = beta 0 1 + Xbeta + epsilon. Recently, the Liu estimator, which is ...
Logistic regression is a widely used method to model categorical response data, and maximum likeliho...
Consider the regression model y = ß01 + Xß + ?. Recently, the Liu estimator, which is an alternative...
In this paper we compare recently developed preliminary test estimator called Preliminary Test Stoch...
Ordinary least squares estimator, mixed estimator, Liu estimator, Stochastic Restricted Liu estimato...
In innovation analysis the logit model used to be applied on available data when the dependent varia...
Multiple linear interferences are a fundamental obstacle in many standard models. This problem appea...
Multiple linear interferences are a fundamental obstacle in many standard models. This problem appea...
Multicollinearity problem in logistic regression causes an inflation in the variance of the Maximum ...
Multicollinearity in logistic regression affects the variance of the maximum likelihood estimator ne...
Multicollinearity negatively affects the efficiency of the maximum likelihood estimator (MLE) in bot...
Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper pr...
In this study, we introduce iterative restricted Liu estimator to combat multicollinearity in genera...
This paper deals with the problem of multicollinearity in a multiple linear regression model with li...
Multicollinearity among the explanatory variables seriously effects the maximum likelihood estimator...
Consider the regression model y = beta 0 1 + Xbeta + epsilon. Recently, the Liu estimator, which is ...
Logistic regression is a widely used method to model categorical response data, and maximum likeliho...
Consider the regression model y = ß01 + Xß + ?. Recently, the Liu estimator, which is an alternative...
In this paper we compare recently developed preliminary test estimator called Preliminary Test Stoch...
Ordinary least squares estimator, mixed estimator, Liu estimator, Stochastic Restricted Liu estimato...
In innovation analysis the logit model used to be applied on available data when the dependent varia...
Multiple linear interferences are a fundamental obstacle in many standard models. This problem appea...
Multiple linear interferences are a fundamental obstacle in many standard models. This problem appea...