Bayesian Inference for Masked System Lifetime Data

B. Reiser; I. Guttman; Dennis K. J. Lin; Frank M. Guess; John S. Usher

doi:10.2307/2986196

Bayesian Inference for Masked System Lifetime Data

B. Reiser, I. Guttman, Dennis K. J. Lin, Frank M. Guess, John S. Usher

Department of Statistics

Research output: Contribution to journal › Article › peer-review

Abstract

Estimating component and system reliabilities frequently requires using data from the system level. Because of cost and time constraints, however, the exact cause of system failure may be unknown. Instead, it may only be ascertained that the cause of system failure is due to a component in a subset of components. This paper develops methods for analysing such masked data from a Bayesian perspective. This work was motivated by a data set on a system unit of a particular type of IBM PS/2 computer. This data set is discussed and our methods are applied to it.

Original language	English
Pages (from-to)	79-90
Journal	Journal of the Royal Statistical Society. Series C: Applied Statistics
Volume	44
Issue number	1
DOIs	https://doi.org/10.2307/2986196
State	Published - 1995

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title = "Bayesian Inference for Masked System Lifetime Data",

abstract = "Estimating component and system reliabilities frequently requires using data from the system level. Because of cost and time constraints, however, the exact cause of system failure may be unknown. Instead, it may only be ascertained that the cause of system failure is due to a component in a subset of components. This paper develops methods for analysing such masked data from a Bayesian perspective. This work was motivated by a data set on a system unit of a particular type of IBM PS/2 computer. This data set is discussed and our methods are applied to it.",

author = "B. Reiser and I. Guttman and Lin, {Dennis K. J.} and Guess, {Frank M.} and Usher, {John S.}",

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AU - Guess, Frank M.

AU - Usher, John S.

PY - 1995

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AB - Estimating component and system reliabilities frequently requires using data from the system level. Because of cost and time constraints, however, the exact cause of system failure may be unknown. Instead, it may only be ascertained that the cause of system failure is due to a component in a subset of components. This paper develops methods for analysing such masked data from a Bayesian perspective. This work was motivated by a data set on a system unit of a particular type of IBM PS/2 computer. This data set is discussed and our methods are applied to it.

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DO - 10.2307/2986196

M3 - Article

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EP - 90

JO - Journal of the Royal Statistical Society. Series C: Applied Statistics

JF - Journal of the Royal Statistical Society. Series C: Applied Statistics

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ER -

Bayesian Inference for Masked System Lifetime Data

Abstract

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