Bootstrap Resampling - Non-parametric confidence intervals for MCDM rankings

Overview

Bootstrap Resampling - Non-parametric confidence intervals for MCDM rankings

Output

robustness score, higher is better

Data

Crisp, complete numeric matrix

Size

2+ alternatives, 3-12 criteria works best

Used for

Sensitivity analysis, uncertainty quantification

How it works

1. 1

   Validate inputs for bootstrap-resampling.

   $${\bar{r}}_i^{\ast }=\frac{1}{B}\sum _{b=1}^B{r}_i^{(b)};\text{95\% CI via percentile method}$$

   Efron 1979, (pending PDF page verification)

Look elsewhere when

Assumptions to verify

• A base ranking method is selected
• Computational budget for repeated runs

Edge cases and pitfalls

• Applying BOOTSTRAP-RESAMPLING without verifying this assumption.

  Requirement: A base ranking method is selected

• Applying BOOTSTRAP-RESAMPLING without verifying this assumption.

  Requirement: Computational budget for repeated runs

• Using BOOTSTRAP-RESAMPLING when: Quick analysis needed → defer robustness check.

  An alternative method is recommended in this situation.

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