A Comparison Between Bootstrap and Bayesian Methods In Estimating the Parameters of the Conditional Logistic Regression Model with A Practical Application

Zahraa Saad Jasim

Authors

Zahraa Saad Jasim AL-Furat AL-AwsatTechnical University, Al-Qadisiyah Polytechnic College, Iraq

Keywords:

Logistic, Bootstrap, Classical estimates, The Clogit (CL), Traditional Evaluation

Abstract

Objects can arrive at a conclusion by two logical and inferential procedures: indistinguishable-to-average consecutive likelihood and traditional evaluation, the test would be satisfactory. This paper tests two kinds of estimation techniques: Bootstrap resampling and Bayesian inference, for estimating the coefficients in a Conditional Logistic Regression model applied to heart disease data. Classical estimates were obtained using the clogit (CL) function, and the Bootstrap estimates came from 100 resampled samples. Both methods give similar estimates, with the Bootstrap method supplying an additional advantage of quantifying variability. There was an attempt of Bayesian estimation but it failed as the program could not properly initialize in the present environment. This gives further support to the use of Bootstrap and Classical methods for practical analysis, and lays a foundation for Bayesian treatment in future development efforts.

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