Logistic Regression Under Sparse Data Conditions
David A. Walker, Thomas J. Smith
Abstract
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.
Topics & Concepts
FirthLogistic regressionStatisticsMathematicsBinary dataEstimationEconometricsRegressionBinary numberEconomicsArithmeticOceanographyGeologyManagementAdvanced Statistical Methods and ModelsMulti-Criteria Decision MakingForecasting Techniques and Applications