1
Faculty Member, Islamic Azad University, Bonab Branch, Department of Industrial Engineering, Islamic Azad University, Bonab, Iran
2
Master of Science in Industrial Engineering, Young Researchers and Elites Club, Ilkhchi Branch, Islamic Azad University, Ilkhchi, Iran
3
Master of Science in Engineering, Islamic Azad University, Bonab Branch, Department of Industrial Engineering, Islamic Azad University, Bonab, Iran
Abstract
The problem of sample size estimation is important in medical applications, especially in the case of expensive biomarker tests. This paper describes the problem of logistic regression analysis with sample size estimation algorithms, which include univariate statistical methods, logistic regression, validity intersection, and Bayesian inference. The authors treat the regression model parameters as multivariate variables with the aim of estimating the sample size using the distance between the parameter distribution functions in the validity intersection datasets. Here, the authors present a new aid for data mining and statistical training supported by applied mathematics.
