稀疏数据条件下的逻辑回归

Q3 Mathematics

Journal of Modern Applied Statistical Methods Pub Date : 2020-09-25 DOI:10.22237/JMASM/1604190660

D. Walker, Thomas J. Smith

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引用次数: 8

摘要

稀疏数据条件的影响在逻辑回归中的一个或多个预测变量中进行了检验，并评估了Firth(1993)程序在减少潜在参数估计偏差方面的有效性。结果表明，在小样本量的情况下，二元预测器的稀疏性引入了大量的偏差，而Firth程序可以有效地纠正这种偏差。

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Logistic Regression Under Sparse Data Conditions

The impact of sparse data conditions was examined among one or more predictor variables in logistic regression and assessed the effectiveness of the Firth (1993) procedure in reducing potential parameter estimation bias. Results indicated sparseness in binary predictors introduces bias that is substantial with small sample sizes, and the Firth procedure can effectively correct this bias.

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来源期刊

Journal of Modern Applied Statistical Methods STATISTICS & PROBABILITY-

CiteScore

0.50

自引率

0.00%

发文量

期刊介绍： The Journal of Modern Applied Statistical Methods is an independent, peer-reviewed, open access journal designed to provide an outlet for the scholarly works of applied nonparametric or parametric statisticians, data analysts, researchers, classical or modern psychometricians, and quantitative or qualitative methodologists/evaluators.