Hitting Times

1978 ◽  
pp. 16-52
Author(s):  
SIDNEY C. PORT ◽  
CHARLES J. STONE
Keyword(s):  
Hitting Times

Expected numbers at hitting times

1991 ◽  
Vol 15 (6) ◽  
pp. 637-648 ◽  
Author(s):  
Colin McDiarmid
Keyword(s):  
Hitting Times

On identifiability and order of continuous-time aggregated Markov chains, Markov-modulated Poisson processes, and phase-type distributions

1996 ◽  
Vol 33 (3) ◽  
pp. 640-653 ◽  
Author(s):  
Tobias Rydén
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Continuous Time ◽  
Poisson Processes ◽  
Hitting Times ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Minimum Number ◽  
Markov Modulated ◽  
Two Parameters

An aggregated Markov chain is a Markov chain for which some states cannot be distinguished from each other by the observer. In this paper we consider the identifiability problem for such processes in continuous time, i.e. the problem of determining whether two parameters induce identical laws for the observable process or not. We also study the order of a continuous-time aggregated Markov chain, which is the minimum number of states needed to represent it. In particular, we give a lower bound on the order. As a by-product, we obtain results of this kind also for Markov-modulated Poisson processes, i.e. doubly stochastic Poisson processes whose intensities are directed by continuous-time Markov chains, and phase-type distributions, which are hitting times in finite-state Markov chains.

scholarly journals Stein's Method for Compound Geometric Approximation

2010 ◽  
Vol 47 (1) ◽  
pp. 146-156 ◽  
Author(s):  
Fraser Daly
Keyword(s):  
Generating Functions ◽  
Analytical Approach ◽  
Geometric Distribution ◽  
Stein’S Method ◽  
Hitting Times ◽  
Stein's Method ◽  
Geometric Approximation ◽  
Compound Geometric Distribution ◽  
Probabilistic Approximation ◽  
Sequence Patterns

We apply Stein's method for probabilistic approximation by a compound geometric distribution, with applications to Markov chain hitting times and sequence patterns. Bounds on our Stein operator are found using a complex analytical approach based on generating functions and Cauchy's formula.

A degradation model for maintenance improvement in respect of cost and availability

2015 ◽  
Vol 21 (1) ◽  
pp. 55-69 ◽  
Author(s):  
Christophe Letot ◽  
Pierre Dehombreux ◽  
Edouard Rivière-Lorphèvre ◽  
Guillaume Fleurquin ◽  
Arnaud Lesage
Keyword(s):  
Preventive Maintenance ◽  
Residual Life ◽  
Operating Time ◽  
Hitting Times ◽  
Mean Residual Life ◽  
Reliability Model ◽  
Content Type ◽  
Degradation Data ◽  
Specific Item ◽  
The Mean

Purpose – The purpose of this paper is to highlight the need for degradation data in order to improve the reliability and the mean residual life estimation of a specific item of equipment and to adapt the preventive maintenance tasks accordingly. Design/methodology/approach – An initial reliability model which uses a degradation-based reliability model that is built from the collection of hitting times of a failure threshold. The proposed maintenance model is based on the cost/availability criterion. The estimation of both reliability and optimum time for preventive maintenance are updated with all new degradation data that are collected during operating time. Findings – An improvement for the occurrences of maintenance tasks which minimizes the mean cost per unit of time and increases the availability. Practical implications – Inspection tasks to measure the degradation level should be realized at least one time for each item of equipment at a specific time determined by the proposed methodology. Originality/value – The introduction of a criterion which helps the maintainer to decide to postpone or not the preventive replacement time depending on the measured degradation level of a specific item of equipment.

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