Reducing Bias in Beta Regression Models Using Jackknifed Liu-Type Estimators: Applications to Chemical Data

Solmaz Seifollahi, Hossein Bevrani, Olayan Albalawi and Niansheng Tang

Journal of Mathematics, 2024, vol. 2024, 1-12

Abstract: In the field of chemical data modeling, it is common to encounter response variables that are constrained to the interval (0, 1). In such cases, the beta regression model is often a more suitable choice for modeling. However, like any regression model, collinearity can present a significant challenge. To address this issue, the Liu-type estimator has been used as an alternative to the maximum likelihood estimator, but it suffers from bias. In this paper, we introduce the Jackknifed Liu-type estimator and its modified version, which demonstrate improved bias reduction compared to the original Liu-type estimator. We assess the theoretical and numerical performance of these estimators through Monte Carlo simulations and real-data examples from the field of chemistry. Our findings highlight the significant improvements offered by the proposed estimators in terms of accuracy and reliability.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:6694880

DOI: 10.1155/2024/6694880

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