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Please use this identifier to cite or link to this item: http://hdl.handle.net/2262/64036

Title: Estimating production test properties from test measurement data
Author: WILSON, SIMON PAUL
Sponsor: 
Name Grant Number
08/IN.1/I1879
03/CE3/I40,5

Author's Homepage: http://people.tcd.ie/swilson
Keywords: Statistics
Imperfect repair
Imperfect test
Gaussian mixture model
parameter estimation
Issue Date: 2011
Publisher: Wiley-Blackwell
Citation: Simon P. Wilson, Suresh Goyal, Estimating production test properties from test measurement data, Applied Stochastic Models in Business and Industry, 2011
Series/Report no.: Applied Stochastic Models in Business and Industry;
Abstract: A complex sequence of tests on components and the system is a part of many manufacturing processes. Statistical imperfect test and repair models can be used to derive the properties of such test sequences but require model parameters to be specified. We describe a technique for estimating such parameters from typical data that are available from past testing. A Gaussian mixture model is used to model the wide variety of statistical properties of test data, including outliers, multi-modality and skewness, from which test properties are derived. Model fitting is through a Bayesian approach, implented by Markov chain Monte Carlo.
Description: PUBLISHED
URI: http://hdl.handle.net/2262/64036
Related links: http://dx.doi.org/10.1002/asmb.930
Appears in Collections:Statistics (Scholarly Publications)

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