ENBIS-25 Conference

Session

Statistical methods, applications and recent developments for the technological field (by SIS) Invited Session

17 Sept 2025, 11:30

Amphitheater 339 (3rd floor)

Conveners

Statistical methods, applications and recent developments for the technological field (by SIS) Invited Session

• Rossella Berni (Department of Statistics, Computer Science, Applications -University of Florence)

5. Distribution Free Change Point Control Charts for Monitoring Time Between Events and Amplitude Data

Michele Scagliarini (University of Bologna)

17/09/2025, 11:30

Statistical Process Monitoring

Monitoring the occurrence of undesirable events, such as equipment failures, quality issues or extreme natural phenomena, requires tracking both the time between events (T) and their magnitude (X). Time Between Events and Amplitude (TBEA) control charts have been developed to monitor these two aspects simultaneously. Traditional approaches assume known distributions for T and X. However, in...

24. Zero patterns in multi-way binary contingency tables with uniform margins

Roberto Fontana

17/09/2025, 12:00

Statistical methods, applications and recent developments for the technological field (by SIS)

We study the problem of transforming a multi-way contingency table into an equivalent table with uniform margins and same dependence structure. This is an old question which relates to recent advances in copula modeling for discrete random vectors. In this work, we focus on multi-way binary tables and develop novel theory to show how the zero patterns affect the existence of the transformation...

40. Reinforcement learning for optimal maintenance aided by degradation models

Antonio Pievatolo (CNR-IMATI)

17/09/2025, 12:30

Statistical methods, applications and recent developments for the technological field (by SIS)

We consider a framework which addresses the search for an optimal maintenance policy of a system by using observed system state data to learn the degradation model on one hand, and by using simulation from the learned model to obtain future states and rewards to update the value function and improve the current policy, on the other hand. We apply this framework to the maintenance of...