ENBIS Spring Meeting 2026

Session

Stochastic Modelling & Statistical Estimation

29 May 2026, 10:00

Conveners

Stochastic Modelling & Statistical Estimation

• massimiliano Giorgio (Università di Napoli Federico II)

8. Advanced FIT-Rate Estimation for Serial Systems

Dr Horst Lewitschnig (Infineon Technologies Austria AG)

29/05/2026, 10:00

Spring Meeting

FIT(failures in time )-rates are a typical reliability measure for the constant part of the bathtub curve for non-repairable systems. The FIT-rate is the parameter of the exponential distribution in units of 109 hours. It is the inverse of the mean times between failures (MTBF). It is assessed at so-called MTBF-tests. For a serial system, FIT-rates of the individual components are added up....

9. Maximum Likelihood Inference for Non-Linear Wiener Processes under Multiple Arithmetic Reduction of Degradation (ARD) Maintenances

Tom Berger (Laboratoire Jean Kuntzmann, Université Grenoble Alpes)

29/05/2026, 10:20

Spring Meeting

As time passes, complex industrial systems suffer degradation phenomena that will inevitably lead them to failures. Several degradation models have been proposed to model these phenomena [5]. The most usual ones are based on stochastic processes such as the Wiener process, the Gamma process or the Inverse Gaussian process. In the case of complex systems that must operate without interruptions...

21. Discretization of the Gamma Process: Structural Properties and Reliability Applications

Elham Mosayebi Omshi (University of Technology of Troyes)

29/05/2026, 10:40

Spring Meeting

The discretization of the gamma process plays an important role in both theoretical investigations and practical implementations of stochastic modeling. The gamma process is a continuous-time, non-decreasing Lévy process with independent increments and is widely used in applications such as reliability engineering, survival analysis, and degradation modeling.

In practice, however, observed...

22. Efficient Post-Shrinkage Estimation Strategies in High-Dimensional Cox’s Proportional Hazards Models

Reza Arabi Belaghi (SLU)

29/05/2026, 11:00

Spring Meeting

Regularization methods such as LASSO, adaptive LASSO, Elastic-Net, and SCAD are widely used for variable selection in statistical modeling. However, these approaches primarily focus on variables with strong effects and often overlook weaker signals, which may lead to biased parameter estimates and reduced predictive performance. To address this limitation, corrected shrinkage strategies have...