DocumentCode :
1020202
Title :
Modeling inefficiencies in a reliability system using stochastic frontier regression
Author :
Chandra, K. Suresh ; Ramanathan, T.V.
Author_Institution :
Dept. of Stat., Univ. of Botswana, Gaborone, Botswana
Volume :
53
Issue :
2
fYear :
2004
fDate :
6/1/2004 12:00:00 AM
Firstpage :
250
Lastpage :
254
Abstract :
For some reliability systems, it is possible to have the system reliability smaller than the reliability obtained using the configuration of the components. This may be due to the inefficiency of the system. By inefficiency, we mean any tendency or attribute that will bring down the performance of the system from the level the configuration is capable of or expected to provide or designed for. This sets a maximum limit (or frontier) for the performance of the system. Therefore, deviation of the observed level from this limit would then be an indicator of the inefficiency. In this paper, we have made an attempt to model inefficiencies in the working of a reliability system, and to define an inefficiency index. The paper discusses the practical estimation of the coefficient of inefficiency in the system performance. The stochastic frontier regression methods are used to estimate the inefficiency. The validity of the methodology has been assessed for an exponential model, using a limited simulation study. The inefficiency indices proposed in this paper are simple, as they must be to be useful to engineers. We found that the suggested indices & their estimation procedures work well.
Keywords :
least mean squares methods; regression analysis; reliability theory; stochastic processes; modeling inefficiency; modified ordinary least square; reliability system; stochastic frontier regression; system performance; Employee welfare; Least squares methods; Maximum likelihood estimation; Productivity; Random variables; Reliability; Statistical distributions; Stochastic processes; Stochastic systems; System performance; Inefficiency in reliability systems; MOLS; modified ordinary least squares; stochastic frontier regression;
fLanguage :
English
Journal_Title :
Reliability, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9529
Type :
jour
DOI :
10.1109/TR.2004.829147
Filename :
1308669
Link To Document :
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