Comparison of two nonparametric estimators for reliability of discrete-time semi-Markov systems based on multiple independent observations

Stylianos Georgiadis; Nikolaos  Limnios

Comparison of two nonparametric estimators for reliability of discrete-time semi-Markov systems based on multiple independent observations

Stylianos Georgiadis, Nikolaos Limnios

Université de technologie de Compiègne

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

We consider a discrete-time semi-Markov system, with a finite state space. The empirical and the exact maximum likelihood estimator for the semi-Markov kernel are given in the case of multiple parallel observations of the same process. Afterwards, we describe a reliability model described by a discrete-time semi-Markov process and we derive basic reliability measures, such as reliability, availability, failure rates and mean hitting times. Finally, we present a comparison between empirical and exact maximum likelihood estimators for these measures through a numerical application.

Original language	English
Title of host publication	Proceedings of the international conference on Statistical Models and Methods for Reliability and Survival Analysis and Their Validation (S2MRSA 2012)
Publication date	2012
Pages	98-102
Publication status	Published - 2012
Externally published	Yes
Event	International Conference on Statistical Models and Methods for Reliability and Survival Analysis and Their Validation - University of Bordeaux, Bordeaux, France Duration: 4 Jul 2012 → 6 Jul 2012

Conference

Conference	International Conference on Statistical Models and Methods for Reliability and Survival Analysis and Their Validation
Location	University of Bordeaux
Country/Territory	France
City	Bordeaux
Period	04/07/2012 → 06/07/2012

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title = "Comparison of two nonparametric estimators for reliability of discrete-time semi-Markov systems based on multiple independent observations",

abstract = "We consider a discrete-time semi-Markov system, with a finite state space. The empirical and the exact maximum likelihood estimator for the semi-Markov kernel are given in the case of multiple parallel observations of the same process. Afterwards, we describe a reliability model described by a discrete-time semi-Markov process and we derive basic reliability measures, such as reliability, availability, failure rates and mean hitting times. Finally, we present a comparison between empirical and exact maximum likelihood estimators for these measures through a numerical application.",

author = "Stylianos Georgiadis and Nikolaos Limnios",

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language = "English",

pages = "98--102",

booktitle = "Proceedings of the international conference on Statistical Models and Methods for Reliability and Survival Analysis and Their Validation (S2MRSA 2012)",

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Georgiadis, S & Limnios, N 2012, Comparison of two nonparametric estimators for reliability of discrete-time semi-Markov systems based on multiple independent observations. in Proceedings of the international conference on Statistical Models and Methods for Reliability and Survival Analysis and Their Validation (S2MRSA 2012). pp. 98-102, International Conference on Statistical Models and Methods for Reliability and Survival Analysis and Their Validation, Bordeaux, France, 04/07/2012.

Comparison of two nonparametric estimators for reliability of discrete-time semi-Markov systems based on multiple independent observations. / Georgiadis, Stylianos; Limnios, Nikolaos .

Proceedings of the international conference on Statistical Models and Methods for Reliability and Survival Analysis and Their Validation (S2MRSA 2012). 2012. p. 98-102.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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BT - Proceedings of the international conference on Statistical Models and Methods for Reliability and Survival Analysis and Their Validation (S2MRSA 2012)

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Comparison of two nonparametric estimators for reliability of discrete-time semi-Markov systems based on multiple independent observations

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