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    Abstract
2017 (Vol. 9, Issue: 2)
Research Article

Evaluating the Performance of Liu Logistic Regression Estimator

Rasha A. Farghali and Samah M. Abo-El-Hadid
Department of Mathematics, Insurance and Applied Statistics, Faculty of Commerce and Business Administration, Helwan University, Cairo, Egypt
 

DOI: 10.19026/rjms.9.4715
Submitted Accepted Published
November 15, 2016 February 13, 2017 November 25, 2017

  How to Cite this Article:

Rasha A. Farghali and Samah M. Abo-El-Hadid, 2017. Evaluating the Performance of Liu Logistic Regression Estimator.  Research Journal of Mathematics and Statistics, 9(2): 11-19.

DOI: 10.19026/rjms.9.4715

URL: http://www.maxwellsci.com/jp/mspabstract.php?jid=RJMS&doi=rjms.9.4715

Abstract:


This study aims at comparing the performance of logistic Liu estimators with Maximum Likelihood (ML), Stien and ridge regression estimators using a Monte Carlo simulation, where the mean squared/absolute errors, MSE(&beta)/MAE(&beta) mean squared/absolute error between the actual probability π(x) and the estimated probability π ̂(x), MSE(π(x))/MAE(π(x)) are used as performance criteria. An algorithm for simulation steps is included. An application of the effect of quantities of household wastes and its components on the probability of getting a running waste recycling factory is analyzed. Results from both the simulation and the application show that logistic Liu estimators are mostly preferred for correcting mutilcollinearity in logistic regression.

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    Competing interests

The authors have no competing interests.
    Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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© The Author(s) 2017

ISSN (Online):  2040-7505
ISSN (Print):   2042-2024
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