scholarly journals On dynamic mutual information for bivariate lifetimes

2015 ◽  
Vol 47 (04) ◽  
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.

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.

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.

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.

Distribution-free monitoring schemes based on order statistics: a general approach

2019 ◽  
Vol 47 (12) ◽  
pp. 2230-2257 ◽  
Author(s):  
Ioannis S. Triantafyllou ◽  
Nikolaos I. Panayiotou
Keyword(s):  
Order Statistics ◽  
Distribution Free

Evaluation of simple quantile estimation functions for modeling forest diameter distributions in even-aged stands of interior Douglas-fir

1993 ◽  
Vol 23 (11) ◽  
pp. 2376-2382 ◽  
Author(s):  
James D. Newberry ◽  
James A. Moore ◽  
Lianjun Zhang
Keyword(s):  
Probability Distribution ◽  
Order Statistics ◽  
Estimation Method ◽  
Douglas Fir ◽  
Quantile Estimation ◽  
Distribution Free ◽  
Inland Northwest ◽  
Distribution Parameters ◽  
Solving Equations ◽  
Diameter Distributions

The method of percentiles usually involves simultaneously solving equations for probability distribution parameters as functions of sample-based estimates of the appropriate quantiles. Eight simple distribution-free methods for estimating quantiles from sample-based order statistics were evaluated empirically using even-aged Douglas-fir (Pseudotsugamenziesii var. glauca (Beissn.) Franco) diameter distributions from the Inland Northwest. Two methods, calculated by weighting adjacent order statistics, consistently gave the best results for both the Weibull and Johnson's SB distributions. Certain distributional shapes were also evaluated to determine if they influenced the quantile estimation method. Although some influence was detected, the best methods were usually best across all categories.

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