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
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