Young Researcher and Innovator Conference Grant

Approximation Laws for Optimal Stopping Under Generalized-Gamma Uncertainty

Moncef Zarrouk, Mohammed V University, Rabat, Morocco
Mohamed Oumoucha, Mohammed V University, Rabat, Morocco
Faissal El Bouanani, Mohammed V University, Rabat, Morocco
Zakaria El Allali, Mohamed First University, Oujda, Morocco

The International Conference on Approximation Theory & Special Functions (ATSF 2026)
Ankara, Türkiye
2-5 September 2026

Grantee: Moncef Zarrouk, Mohammed V University, Rabat, Morocco
Type of publication: Abstract
Type of presentation: Oral

Start date: 2026-09-02
End date: 2026-09-05
Awarded: 2026-07-30

Abstract

This work develops finite approximation laws for a class of optimal-stopping problems under generalized-gamma uncertainty. A finite sequence of candidates is observed sequentially, with each candidate associated with a random performance variable governed by a generalized-gamma distribution. The objective is to determine an optimal stopping rule for accepting a candidate while controlling observation costs and terminal failure risk. By exploiting the flexibility of the generalized-gamma model, we derive tractable expressions for the stopping probability, failure probability, expected stopping time, and expected observation cost. The resulting formulas provide a compact analytical description of the tradeoff between distributional shape, decision quality, and sampling effort. Numerical illustrations validate the proposed approximation laws and show how the generalized-gamma parameters affect stopping behavior.