Marco Capaldo, Antonio Di Crescenzo, Franco Pellerey
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Mean Distances and Dependence Structures for Lifetimes of Systems With Shared Components
Distortion and copula functions represent powerful tools in the description of the reliability of some complex systems as functions of their components' reliability. On this aim, we study several pairs of reliability systems with one or more shared components, in the case when their lifetimes are independent and identically distributed or independent but not identically distributed. We focus on the dependence that arises from sharing components, often described by Marshall-Olkin copulas, making use of some distance measures related to the Gini's mean difference and its new recent generalizations. A special role is played by a new distortion function related to the ROC curve.
期刊介绍：
ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process.
The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
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