Bootstrapping the Confidence Intervals of R2 MAD for Samples from Contaminated Standard Logistic Distribution

This paper investigates the confidence intervals of r^2mad, the coefficient of determination based onmedian absolute deviation in the presence of outliers. bootstrap bias-corrected accelerated (bca) confidence intervals, known to have higher degree of correctness, are constructed for the mean andstandard deviation of r^2 mad for samples generated from contaminated standard logistic distribution. the results indicate that by increasing the sample size and percentage of contaminants in the samples, and perturbing the location and scale of the distribution affect the lengths of the confidence intervals. the results obtained can also be used to verify the bound of r^2 mad.