A new alternative estimation method for Liu-type logistic estimator via particle swarm optimization: an application to data of collapse of Turkish commercial banks during the Asian financial crisis. Issue 13 (18th November 2021)

• Record Type: Journal Article
• Authors: Sancar, Nuriye; Inan, Deniz
• Is Part Of: Journal of applied statistics. Volume 48:Issue 13/14/15(2021)
• Page Start: 2499
• Page End: 2514
• Publication Date: 2021-11-18
• Subjects: Logistic Liu-type estimator -- ridge logistic estimator -- maximum likelihood estimator -- multicollinearity -- particle swarm optimization; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2020.1837085
• Languages: English
• ISSNs: 0266-4763

Abstract

Abstract : In the existence of multicollinearity problem in the logistic model, some important problems may occur in the analysis of the model, such as unstable maximum likelihood estimator with very high standard errors, false inferences. The Liu-type logistic estimator was proposed as two-parameter estimator to overcome multicollinearity problem in the logistic model. In the existing previous studies, the ( k, d ) pair in this shrinkage estimator is estimated by two-phase methods. However, since the different estimators can be utilized in the estimation of d, optimal choice of the ( k, d ) pair provided using the two-phase approaches is not guaranteed to overcome multicollinearity. In this article, a new alternative method based on particle swarm optimization is suggested to estimate ( k, d ) pair in Liu-type logistic estimator, simultaneously. For this purpose, an objective function that eliminates the multicollinearity problem, provides minimization of the bias of the model and improvement of the model's predictive performance, is developed. Monte Carlo simulation study is conducted to show the performance of the proposed method by comparing it with existing methods. The performance of the proposed method is also demonstrated by the real dataset which is related to the collapse of commercial banks in Turkey during Asian financial crisis.