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<p>Logistic regression model applications have become very popular in the analysis of social, economy, finance, agriculture, health economics and medical science in recent years. However, if there is a high degree of relationship between the independent variables, the problem of multicollinearity arises in this model. In this paper, we introduce a new Jackknifed two-parameter estimator (JTPE) and the two-parameter estimator (TPE) for the logistic regression model by unifying the JTPE of Kandemir Çetinkaya and Kaçranlar (Biased estimators and their applications in generalized linear models. Thesis, 2024). We examined bias vectors and matrix mean squared error (MMSE) of the TPE and the JTPE. The generalization of some estimation methods for ridge and Liu parameters in logistic regression model are provided. Also, the superiority of JTPE is assessed by the simulated mean squared error (SMSE) via Monte Carlo simulation study where the response follows logistic regression model. We finally consider real data applications. The proposed estimators are compared and interpreted.</p>

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A new estimator for the multicollinear logistic regression model

  • 期刊论文

  • 发表日期：

    2025年2月20日

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  • Merve Kandemir Çetinkaya

摘要

Logistic regression model applications have become very popular in the analysis of social, economy, finance, agriculture, health economics and medical science in recent years. However, if there is a high degree of relationship between the independent variables, the problem of multicollinearity arises in this model. In this paper, we introduce a new Jackknifed two-parameter estimator (JTPE) and the two-parameter estimator (TPE) for the logistic regression model by unifying the JTPE of Kandemir Çetinkaya and Kaçranlar (Biased estimators and their applications in generalized linear models. Thesis, 2024). We examined bias vectors and matrix mean squared error (MMSE) of the TPE and the JTPE. The generalization of some estimation methods for ridge and Liu parameters in logistic regression model are provided. Also, the superiority of JTPE is assessed by the simulated mean squared error (SMSE) via Monte Carlo simulation study where the response follows logistic regression model. We finally consider real data applications. The proposed estimators are compared and interpreted.