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Performance of Some Weighted Liu Estimators for Logit Regression Model: An Application to Swedish Accident Data

Author

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  • Kristofer Månsson
  • B. M. Golam Kibria
  • Ghazi Shukur

Abstract

In this article, we propose some new estimators for the shrinkage parameter d of the weighted Liu estimator along with the traditional maximum likelihood (ML) estimator for the logit regression model. A simulation study has been conducted to compare the performance of the proposed estimators. The mean squared error is considered as a performance criteria. The average value and standard deviation of the shrinkage parameter d are investigated. In an application, we analyze the effect of usage of cars, motorcycles, and trucks on the probability that pedestrians are getting killed in different counties in Sweden. In the example, the benefits of using the weighted Liu estimator are shown. Both results from the simulation study and the empirical application show that all proposed shrinkage estimators outperform the ML estimator. The proposed D9 estimator performed best and it is recommended for practitioners.

Suggested Citation

  • Kristofer Månsson & B. M. Golam Kibria & Ghazi Shukur, 2015. "Performance of Some Weighted Liu Estimators for Logit Regression Model: An Application to Swedish Accident Data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(2), pages 363-375, January.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:2:p:363-375
    DOI: 10.1080/03610926.2012.745562
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    Cited by:

    1. Issam Dawoud & B. M. Golam Kibria, 2020. "A New Biased Estimator to Combat the Multicollinearity of the Gaussian Linear Regression Model," Stats, MDPI, vol. 3(4), pages 1-16, November.

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