Abstract
This research addresses the issue of near-multicollinearity within the nonlinear regression framework, specifically the multiple logistic regression model, where the dependent variable is qualitative and binary (representing the occurrence or non-occurrence of a response). The study utilizes Iterative Principal Component Estimators (IPCE) based on both standard weights and conditional Bayesian weights. These estimators were applied to analyze the effects of two drug concentrations, Ciprodar and Garaycin, on patients with pyelonephritis (kidney inflammation), where the dependent variable reflects the clinical outcome (recovery vs. non-recovery). Based on the Mean Squared Error (MSE) criterion, the results demonstrate that the Iterative Principal Component Estimators employing conditional Bayesian weights outperform those relying on standard weights, offering higher precision in the presence of multicollinearity.
DOI
10.33095/jeas.v24i109.1566
Subject Area
Statistical
First Page
535
Last Page
544
Recommended Citation
Shemail, A. H. (2018). Comparison Between Normal Weights and Conditional Bays Weights in Iterative Principal Component Estimators. Journal of Economics and Administrative Sciences, 24(109), 535-544. https://doi.org/10.33095/jeas.v24i109.1566
