Jackknife Resampling Method for Estimation of Fuzzy Regression Parameters and Revised Tanaka Method - Topuz, Dervis (Progress in Nutrition, 2021)
Objective: In this paper, hierarchical ways of constructing a fuzzy regression model using the jackknife resampling technique were the basis of the study. Fuzzy techniques are based on blurring the coefficients, while the jackknife technique is based on the delete one and delete-d observations. Methods: This study aimed to estimate the deviation, standard error, and confidence interval of the regression coefficients calculated by the jackknife Revised Tanaka (JFLR) technique, and to compare the performance of the jackknife least squares (JOLSR) technique with the relevant estimates. Results: The calculation of estimates is presented with a clinical numerical example. The deviation of the Revised Tanaka FLR model, standard errors, and confidence intervals of regression coefficients were found to be significantly smaller than the estimated JOLSR standard errors. The value of MSE calculated by fuzzy jackknife regression based on Revised Tanaka FLR technique was found to be bigger than MSE calculated by JOLSR technique. Conclusion: Jackknife OLS and jackknife FLR regression methods can be used effectively for parameter estimation, and the jackknife revised Tanaka regression method gives more reliable and valid results.