When Conservatism Wins: Rare-Event Estimation Depends on What You Fear Missing
TL;DR for operators A model-risk team estimating failures too rare for ordinary sampling has to decide which kind of error it can least afford. The benchmark shows that this choice can reverse which estimator looks best. :contentReference[oaicite:0]{index=0} Under approximately symmetric penalties, GA-AMLS has much lower average SPB loss than QLD for the 1-layer and 4-layer models. When the loss instead makes underestimation much more costly, QLD sharply outperforms GA-AMLS—even though QLD systematically overestimates and GA-AMLS has lower bias. ...