2001
DOI: 10.1006/jmva.2000.1906
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Stochastic Bounds and Dependence Properties of Survival Times in a Multicomponent Shock Model

Abstract: Consider a system that consists of several components. Shocks arrive according to a counting process (which may be non-homogeneous and with correlated interarrival times) and each shock may simultaneously destroy a subset of the components. Shock models of this type arise naturally in reliability modeling in dynamic environments. Due to correlated shock arrivals, individual component lifetimes are statistically dependent, which makes the explicit evaluation of the joint distribution intractable. To facilitate … Show more

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Cited by 22 publications
(15 citation statements)
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“…Looking at the second integral, we must next show that we can conclude that (22) is true, so that (20) is also true, upon summing (21) and (22).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Looking at the second integral, we must next show that we can conclude that (22) is true, so that (20) is also true, upon summing (21) and (22).…”
Section: Discussionmentioning
confidence: 99%
“…Two processes impact a single component each while the third one impacts both components simultaneously. This model is fundamental in reliability theory and it remains an important source of inspiration for much research; see, e.g., [4,5,13,18,21,22,36]. In cumulative shock models [25], a shock simultaneously increases some intrinsic characteristics (such as hazard rate, deterioration level, age, etc.)…”
Section: Introductionmentioning
confidence: 99%
“…In recent years this theory and its applications were extended to dependence ordering of stochastic processes; for examples with state spaces R n or subsets thereof, see [7] and [17], and for a more general approach to Markov processes in discrete and continuous time with general partially ordered state space, see [4].…”
Section: Introductionmentioning
confidence: 99%
“…These results allow to obtain bounds for stochastic models with stationary (not necessary renewal) input stream. They extend results for example of Li and Xu [15,16]. As a byproduct we obtain regularity properties of sequences of stationary random variables which extend results for the iid case [18,28].…”
Section: Introductionmentioning
confidence: 56%