Shock models governed by the mixed poisson process with Pareto mixing variable

In this paper, we first consider the mixed Poisson process with Pareto mixing variable (MPPP) studied by [Citation1] and explore its additional properties. We study the asymptotic behavior of the coefficient of variation and the weak positive dependence property of the MPPP. These properties make the MPPP suitable in real-life situations, and the same is explored in the application part of this paper. Next, we derive the distribution of the first-passage time, the first waiting time, the arrival time for the kth event, and the interarrival times for the MPPP. We also explore the application of the MPPP in shock modeling considering three different classes of shocks, namely the extreme shock model, the cumulative shock model, and the δ-shock model. The survival function, expected value, and variance of failure time are obtained for all the treated cases in the shock model. Further, we perform a sensitivity analysis to investigate the effect of model parameters on system reliability for the extreme shock model and δ-shock model.

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Publication Details

Journal
Stochastic Analysis and Applications
Published
2026-09-04
DOI
https://doi.org/10.1080/07362994.2026.2714801
Primary Topic
Probability and Risk Models
Type
article
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Shock models governed by the mixed poisson process with Pareto mixing variable

Aditya Maheshwari, Ashok Kumar Pathak, Shilpa Garg
Stochastic Analysis and Applications
Probability and Risk Models
article

Shock models governed by the mixed poisson process with Pareto mixing variable

Aditya Maheshwari, Ashok Kumar Pathak, Shilpa Garg
article en

Abstract

In this paper, we first consider the mixed Poisson process with Pareto mixing variable (MPPP) studied by [Citation1] and explore its additional properties. We study the asymptotic behavior of the coefficient of variation and the weak positive dependence property of the MPPP. These properties make the MPPP suitable in real-life situations, and the same is explored in the application part of this paper. Next, we derive the distribution of the first-passage time, the first waiting time, the arrival time for the kth event, and the interarrival times for the MPPP. We also explore the application of the MPPP in shock modeling considering three different classes of shocks, namely the extreme shock model, the cumulative shock model, and the δ-shock model. The survival function, expected value, and variance of failure time are obtained for all the treated cases in the shock model. Further, we perform a sensitivity analysis to investigate the effect of model parameters on system reliability for the extreme shock model and δ-shock model.

Stochastic Analysis and Applications
University of Central Punjab (PK), Indian Institute of Management Indore (IN)
Openalex Percentile: Top 6%
Probability and Risk Models
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Shock models governed by the mixed poisson process with Pareto mixing variable — Aditya Maheshwari, Ashok Kumar Pathak, et al. · Stochastic Analysis and Applications (2026) | TGRS Research Map | TGRS