• Corpus ID: 243833058

Liu Estimator in the Multinomial Logistic Regression Model

@inproceedings{Asar2021LiuEI,
  title={Liu Estimator in the Multinomial Logistic Regression Model},
  author={Yasin Asar and Murat Ericsouglu},
  year={2021}
}
This paper considers the Liu estimator in the multinomial logistic regression model. We propose some different estimators of the biasing parameter. The mean square error (MSE) is considered as the performance criterion. In order to compare the performance of the estimators, we performed a Monte Carlo simulation study. According to the results of the simulation study, we found that increasing the correlation between the independent variables and the number of regressors has a negative effect on… 

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References

SHOWING 1-10 OF 29 REFERENCES

Performance of some ridge regression estimators for the multinomial logit model

ABSTRACT This article considers several estimators for estimating the ridge parameter k for multinomial logit model based on the work of Khalaf and Shukur (2005), Alkhamisi et al. (2006), and Muniz

On Liu Estimators for the Logit Regression Model

Liu-Type Multinomial Logistic Estimator

Multicollinearity in multinomial logistic regression affects negatively on the variance of the maximum likelihood estimator. That leads to inflated confidence intervals and theoretically important

A Simulation Research on a Biased Estimator in Logistic Regression Model

In this article, a biased estimator is proposed to combat multi-collinearity in the logistic regression model. The proposed estimator is a general estimator which includes other biased estimators,

New Shrinkage Parameters for the Liu-type Logistic Estimators

New shrinkage parameters for the Liu-type estimators in the Liu (2003) in the logistic regression model defined by Huang (2012) are introduced in order to decrease the variance and overcome the problem of multicollinearity.

Liu-Type Logistic Estimator

The primary interest of this article is to introduce a Liu-type estimator that had a smaller total mean squared error (MSE) than the Schaefer's ridge estimator under certain conditions.

On Ridge Parameters in Logistic Regression

This article applies and investigates a number of logistic ridge regression (RR) parameters that are estimable by using the maximum likelihood (ML) method. By conducting an extensive Monte Carlo

Improved ridge regression estimators for the logistic regression model

Five ridge regression estimators are proposed, namely, unrestricted RR, restricted ridge regression, preliminary test RR, shrinkage ridge regression and positive rule RR estimators for estimating the parameters when it is suspected that the parameter H =h may belong to a linear subspace defined by $$H\beta =h$$.

On Developing Ridge Regression Parameters: A Graphical investigation

In this paper we have reviewed some existing and proposed some new estimators for estimating the ridge parameter "k" . All in all 19 different estimators have been studied. The investigation has been

A Monte Carlo Study of Recent Ridge Parameters

A new approach to obtain the ridge parameter K is suggested and then evaluated by Monte Carlo simulations and it is shown that at least one of the proposed estimators have a smaller MSE than the ordinary least squared estimator (OLS) and Hoerl and Kennard (1970a) estimators (HK).