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Analysis of interval‐censored data with random unknown end points: an application to soft error rate estimation
Author(s) -
Michalak Sarah E.,
Hamada Michael S.,
Hengartner Nicolas W.
Publication year - 2013
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12005
Subject(s) - interval (graph theory) , soft error , event (particle physics) , bayesian probability , inference , interval estimation , statistics , computer science , algorithm , process (computing) , bayesian inference , observational error , random error , data mining , confidence interval , mathematics , artificial intelligence , engineering , physics , electronic engineering , quantum mechanics , combinatorics , operating system
Summary. The paper presents a Bayesian approach to analysing interval‐censored data with random unknown end points. Such data occur when the event of interest is interval censored but, because of the measurement process, the interval end points are not known exactly. Modelling the measurement process permits inference that accounts for this source of variability. Our results are motivated by an experimental study that was designed to characterize the cosmic‐ray–neutron‐induced soft error rate of a semiconductor device.