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- W2373399233 abstract "Mixed-effects models,also called random-effects models,are a regression type of analysis which enables the analyst to not only describe the trend over time within each subject,but also to describe the variation among different subjects.Nonlinear mixed-effects models provide a powerful and flexible tool for handling the unbalanced count data.In this paper,nonlinear mixed-effects models are used to analyze the failure data from a repairable system with multiple copies.By using this type of models,statistical inferences about the population and all copies can be made when accounting for copy-to-copy variance.Results of fitting nonlinear mixed-effects models to nine failure-data sets show that the nonlinear mixed-effects models provide a useful tool for analyzing the failure data from multi-copy repairable systems." @default.
- W2373399233 created "2016-06-24" @default.
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- W2373399233 date "2007-01-01" @default.
- W2373399233 modified "2023-09-26" @default.
- W2373399233 title "Nonlinear Mixed-Effects Models for Repairable Systems Reliability" @default.
- W2373399233 hasPublicationYear "2007" @default.
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