What is the Value of Censored Data? An Exact Analysis for the Data-driven Newsvendor
This work provides an exact performance analysis for data-driven newsvendor policies when demand data is censored. The researchers reduced a complex, infinite-dimensional optimization problem to a finite one, allowing them to precisely calculate the worst-case regret for any sample size and censoring level. This reveals how limited exploration can dramatically improve policy guarantees under uncertainty.