Robustness
Bootstrap Resampling: Non-parametric confidence intervals for MCDM rankings
Efron, B. · 1979
Overview
Robustness wrapper: bootstrap confidence interval estimation. Output typically robustness_score (higher value = preferred).
Strengths
- •Method-specific: Robustness wrapper: bootstrap confidence interval estimation
Limitations
- •Assumes: A base ranking method is selected
- •Assumes: Computational budget for repeated runs
Method assistant
Grounded explanations: it explains the method, it does not compute.
Assumptions to verify
- •A base ranking method is selected
- •Computational budget for repeated runs
When not to use
- •Quick analysis needed → defer robustness check
Edge cases
- •See F.steps and D.parameters for BOOTSTRAP-RESAMPLING-specific edge handling. recommendation_metadata.assumptions_to_verify also lists boundary conditions.
Common pitfalls
- •Hatalı: 'BOOTSTRAP-RESAMPLING bu varsayımı kontrol etmeden uygulamak'. Doğrusu: A base ranking method is selected
- •Hatalı: 'BOOTSTRAP-RESAMPLING bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Computational budget for repeated runs
- •Hatalı: BOOTSTRAP-RESAMPLING'yi 'Quick analysis needed → defer robustness check' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
Worked example
- 1.Adım 1 (F1): Validate inputs for bootstrap-resampling. Formül: \bar{r}_i^*=\frac{1}{B}\sum_{b=1}^B r_i^{(b)};\quad\text{95\% CI via percentile method} Anchor: Efron 1979, (pending PDF page verification)
How to cite
Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics. https://doi.org/10.1214/aos/1176344552