In this paper, the quantile function is recalled and some reliability measures are rewritten in terms of quantile function. Next, quantile based dynamic cumulative residual entropy is obtained and some of its properties are presented. Then, some characterization results of uniform, exponential and Pareto distributions based on quantile based dynamic cumulative entropy are provided. A simple estimator is also proposed and its performance is studied for exponential distribution. Finally discussion and results are presented.

Determining criterion weights in multi-criteria decision-making using Shannon entropy faces significant limitations, stemming from its sensitivity to outliers and ambiguities in probability estimation, which can compromise the validity of the results. This paper introduces a new model based on cumulative residual entropy, offering a more robust and transparent weighting method that is resilient to skewed data. Its application to performance-based budgeting in Iranian universities demonstrates that, when combined with subjective approaches like the Delphi method, this technique yields more accurate estimates of actual budgetary requirements. Furthermore, by mitigating bias caused by outliers, the model prevents the excessive concentration of resources in large universities; instead, through a more equitable redistribution of weights, it facilitates budget allocation that favors developing universities.