•  
  •  
 

Title (Arabic)

المقارنة بين الاوزان الاعتيادية والاوزان البيزية الشرطية في مقدرات المركبات الرئيسية التكرارية

DOI

10.33095/jeas.v24i109.1566

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.

Abstract (Arabic)

: This paper discusses the problem of semi maulticollinearity in the nonlinear regression model (the multi-logistic regression model) When the dependent variable is a qualitative variable, the binary response is either equal to one for a response or zero for no response, Through the use of Iterative principal component estimatorsWhich are based on the normal weights and conditional Bays weights . If the appliede Estimates this model Through the use of two types of drugs concentrations thy concentration of ciprodar (variable X1) On a number of people with Patients with renal disease represent the dependent variable (The person heals from the disease , The person has not recovered from the disease)from through Mean Error Squares (MSE) The results were indicative of Iterative principal component estemaite Depending on the conditional Bays weights prefer the Iterative principal component estimators Depending on the the normal weights.

First Page

535

Download

Share

COinS