Likelihood Confidence Intervals When Only Ranges Are Available

Abstract

Research papers represent an important and rich source of comparative data. The change is to extract the information of interest. Herein, we look at the possibilities to construct confidence intervals for sample averages when only ranges are available with maximum likelihood estimation with order statistics (MLEOS). Using Monte Carlo simulation, we looked at the confidence interval coverage characteristics for likelihood ratio and Wald-type approximate 95% confidence intervals. We saw indication that the likelihood ratio interval had better coverage and narrower intervals. For single parameter distributions, MLEOS is directly applicable. For location-scale distribution is recommended that the variance (or combination of it) to be estimated using standard formulas and used as a plug-in.

Confounder-adjusted MRI-based predictors of multiple sclerosis disability

“…Differences between HV and MS cohorts in unadjusted and confounder-adjusted MRI biomarkers were analyzed using unpaired two-samples Wilcoxon signed-rank test in R ( 20 ).…”

“…Correlations between measured and predicted clinical scores were evaluated using linear regression models in R Studio version 4.1.0 ( 22 , 23 ), reporting R 2 , Spearman Rho (the relationship strength of the predicted and measured scores) from R CRAN Package: stats ( 20 ) and rstatix ( 24 ); and Concordance Correlation Coefficient (CCC; the degree of reliability in the method when comparing two measurements of the same variable) from R CRAN Package: DescTools ( 25 ).…”