An improved ridge type estimator for logistic regression

Q4 Mathematics

Statistics in Transition Pub Date : 2022-09-01 DOI:10.2478/stattrans-2022-0033

N. Varathan

引用次数: 0

Abstract

Abstract In this paper, an improved ridge type estimator is introduced to overcome the effect of multi-collinearity in logistic regression. The proposed estimator is called a modified almost unbiased ridge logistic estimator. It is obtained by combining the ridge estimator and the almost unbiased ridge estimator. In order to asses the superiority of the proposed estimator over the existing estimators, theoretical comparisons based on the mean square error and the scalar mean square error criterion are presented. A Monte Carlo simulation study is carried out to compare the performance of the proposed estimator with the existing ones. Finally, a real data example is provided to support the findings.

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一种改进的logistic回归岭型估计量

摘要为了克服逻辑回归中多重共线性的影响，提出了一种改进的脊型估计。所提出的估计量称为改进的几乎无偏岭逻辑估计量。它是由脊估计和几乎无偏脊估计相结合得到的。为了评价所提估计量相对于现有估计量的优越性，给出了基于均方误差和标量均方误差准则的理论比较。通过蒙特卡罗仿真研究，比较了所提估计器与现有估计器的性能。最后，给出了一个真实的数据例子来支持研究结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistics in Transition Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

9 weeks

期刊介绍： Statistics in Transition (SiT) is an international journal published jointly by the Polish Statistical Association (PTS) and the Central Statistical Office of Poland (CSO/GUS), which sponsors this publication. Launched in 1993, it was issued twice a year until 2006; since then it appears - under a slightly changed title, Statistics in Transition new series - three times a year; and after 2013 as a regular quarterly journal." The journal provides a forum for exchange of ideas and experience amongst members of international community of statisticians, data producers and users, including researchers, teachers, policy makers and the general public. Its initially dominating focus on statistical issues pertinent to transition from centrally planned to a market-oriented economy has gradually been extended to embracing statistical problems related to development and modernization of the system of public (official) statistics, in general.