Improved Estimation in Logistic Regression through Quadratic Bootstrap Approach: An Application in Agricultural Ergonomics

Abstract

In this paper, quadratic bootstrap estimation procedure for improved estimation under logistic regression set up with one regressor based on Claeskens et al. (2003) for classification of data pertaining to the area of agricultural ergonomics have been discussed. Here, presence or absence of discomfort for the farm labourers in operating farm machineries has been considered as the dependent variable. A comparison in terms of confidence interval and classificatory ability of the logistic regression model between the usual maximum likelihood estimator and the quadratic bootstrap based estimator have been made based on real experimental situation in the field of agricultural ergonomics. The performance of quadratic bootstrap based estimator has been found to be better both in terms of length of the confidence interval of the parameter and classificatory ability of the model. Further, a bias corrected estimator based on quadratic bootstrap estimator following Claeskens et al. (2003) has also been obtained. A simulation study has been carried out which illustrates the improvement of bias corrected estimation over the usual maximum likelihood approach in terms of mean square error of the estimators and efficiency factor.