scholarly journals Evolution of the Dependence of Residual Lifetimes

2013 ◽  
pp. 305-312
Author(s):  
Fabrizio Durante ◽  
Rachele Foschi
Keyword(s):  
Residual Lifetimes

Insensitivity of steady-state distributions of generalized semi-Markov processes by speeds

1978 ◽  
Vol 10 (04) ◽  
pp. 836-851 ◽  
Author(s):  
R. Schassberger
Keyword(s):  
Steady State ◽  
General System ◽  
State Distribution ◽  
Lifetime Distributions ◽  
State Dependent ◽  
Semi Markov Process ◽  
Semi Markov Processes ◽  
Infinite State ◽  
State Case ◽  
Residual Lifetimes

A generalized semi-Markov process with speeds describes the fluctuation, in time, of the state of a certain general system involving, at any given time, one or more living components, whose residual lifetimes are being reduced at state-dependent speeds. Conditions are given for the stationary state distribution, when it exists, to depend only on the means of some of the lifetime distributions, not their exact shapes. This generalizes results of König and Jansen, particularly to the infinite-state case.

scholarly journals On dynamic mutual information for bivariate lifetimes

2015 ◽  
Vol 47 (4) ◽  
pp. 1157-1174 ◽  
Author(s):  
Jafar Ahmadi ◽  
Antonio Di Crescenzo ◽  
Maria Longobardi
Keyword(s):  
Mutual Information ◽  
Order Statistics ◽  
Random Sample ◽  
Exponential Model ◽  
Multicomponent Systems ◽  
Distribution Free ◽  
Lifetime Distributions ◽  
Different Ages ◽  
Special Case ◽  
Residual Lifetimes

We consider dynamic versions of the mutual information of lifetime distributions, with a focus on past lifetimes, residual lifetimes, and mixed lifetimes evaluated at different instants. This allows us to study multicomponent systems, by measuring the dependence in conditional lifetimes of two components having possibly different ages. We provide some bounds, and investigate the mutual information of residual lifetimes within the time-transformed exponential model (under both the assumptions of unbounded and truncated lifetimes). Moreover, with reference to the order statistics of a random sample, we evaluate explicitly the mutual information between the minimum and the maximum, conditional on inspection at different times, and show that it is distribution-free in a special case. Finally, we develop a copula-based approach aiming to express the dynamic mutual information for past and residual bivariate lifetimes in an alternative way.

Censored quantile regression for residual lifetimes

2011 ◽  
Vol 18 (2) ◽  
pp. 177-194 ◽  
Author(s):  
Mi-Ok Kim ◽  
Mai Zhou ◽  
Jong-Hyeon Jeong
Keyword(s):  
Quantile Regression ◽  
Censored Quantile Regression ◽  
Residual Lifetimes

scholarly journals Dynamic Reliability Modeling of Three-State Networks

2014 ◽  
Vol 51 (4) ◽  
pp. 999-1020 ◽  
Author(s):  
S. Ashrafi ◽  
M. Asadi
Keyword(s):  
Network Structure ◽  
Single Step ◽  
Two Dimensional ◽  
Reliability Modeling ◽  
Stochastic Orderings ◽  
Dynamic Reliability ◽  
Network States ◽  
Dynamic Signature ◽  
Stochastic Properties ◽  
Residual Lifetimes

This paper is an investigation into the reliability and stochastic properties of three-state networks. We consider a single-step network consisting of n links and we assume that the links are subject to failure. We assume that the network can be in three states, up (K = 2), partial performance (K = 1), and down (K = 0). Using the concept of the two-dimensional signature, we study the residual lifetimes of the networks under different scenarios on the states and the number of failed links of the network. In the process of doing so, we define variants of the concept of the dynamic signature in a bivariate setting. Then, we obtain signature based mixture representations of the reliability of the residual lifetimes of the network states under the condition that the network is in state K = 2 (or K = 1) and exactly k links in the network have failed. We prove preservation theorems showing that stochastic orderings and dependence between the elements of the dynamic signatures (which relies on the network structure) are preserved by the residual lifetimes of the states of the network (which relies on the network ageing). Various illustrative examples are also provided.

Univariate and multivariate stochastic orderings of residual lifetimes of live components in sequential (𝑛-𝑟+ 1)-out-of-𝑛 systems

2018 ◽  
Vol 55 (3) ◽  
pp. 834-844
Author(s):  
Ghobad Barmalzan ◽  
Abedin Haidari ◽  
Narayanaswamy Balakrishnan
Keyword(s):  
Order Statistics ◽  
Exponential Distribution ◽  
Sufficient Conditions ◽  
Sequential Order ◽  
Stochastic Orderings ◽  
Sequential Order Statistics ◽  
Residual Lifetimes

Abstract Sequential order statistics can be used to describe the ordered lifetimes of components of a system when the failure of a component may affect the reliability of the remaining components. After a reliability system consisting of n components fails, some of its components may still be alive. In this paper we first establish some univariate stochastic orderings and ageing properties of the residual lifetimes of the live components in a sequential (n-r+1)-out-of-n system. We also obtain a characterizing result for the exponential distribution based on uncorrelated residual lifetimes of live components. Finally, we provide some sufficient conditions for comparing vectors of residual lifetimes of the live components from two sequential (n-r+1)-out-of-n systems. The results established here extend some well-known results in the literature.

On the role of dependence in residual lifetimes

2019 ◽  
Vol 153 ◽  
pp. 56-64 ◽  
Author(s):  
Maria Longobardi ◽  
Franco Pellerey
Keyword(s):  
Residual Lifetimes

Stochastic comparisons for residual lifetimes and Bayesian notions of multivariate ageing

1999 ◽  
Vol 31 (4) ◽  
pp. 1078-1094 ◽  
Author(s):  
Bruno Bassan ◽  
Fabio Spizzichino
Keyword(s):  
Traditional Approach ◽  
Stochastic Comparisons ◽  
One Dimensional ◽  
Residual Lifetimes

We compare distributions of residual lifetimes of dependent components of different age. This approach yields several notions of multivariate ageing. A special feature of our notions is that they are based on one-dimensional stochastic comparisons. Another difference from the traditional approach is that we do not condition on different histories.

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