Stochastic Models in Probability and Statistics

Stochastic Models in Probability and Statistics

Aims and Scope

Stochastic Models in Probability and Statistics (SMPS) publishes original research articles that advance the theory, methodology, and applications of stochastic modeling, probability, and statistics, with particular emphasis on reliability theory and related fields. The journal welcomes rigorous theoretical contributions, methodological developments, and applied research addressing complex problems in reliability engineering, lifetime analysis, industrial systems, and related areas.

The scope of the journal includes, but is not limited to, the following topics:

  • Reliability theory and mathematical methods in reliability
  • Lifetime data analysis, survival analysis, and models of ageing and life extension
  • Statistical modeling and inference in reliability engineering
  • Stochastic models in industrial engineering, systems engineering, and operations research
  • Optimization and stochastic modeling for maintenance and replacement policies
  • Quality control, statistical process monitoring, and reliability assessment
  • Stochastic processes, stochastic control, and their applications in reliability
  • Nonparametric and semiparametric methods for lifetime and survival data
  • Statistical methods in information theory and reliability-related data analysis
  • Ordered data models, order statistics, stochastic orders, and their applications
  • Software reliability modeling and assessment
  • Copula methods, dependence modeling, and multivariate reliability analysis
  • Big data analytics, computational statistics, and data-driven approaches to reliability problems

The journal aims to foster the development and dissemination of innovative stochastic and statistical methods, promote the integration of theoretical advances with practical applications, and provide a forum for researchers working on emerging challenges in reliability and related disciplines. Contributions that demonstrate methodological originality, mathematical rigor, and meaningful applications to real-world problems are particularly encouraged.