Order restricted inference for sequential k-out-of-n systems
By Narayanaswamy Balakrishnan, E. Beutner, Udo Kamps. 2008
55 citations recorded by the source corpus.
Sequential order statistics have been introduced to model sequential k-out-of-n systems which, as an extension of k-out-of-n systems, allow the failure of some components of the system to influence the remaining ones. Based on an independent sample of vectors of sequential order statistics, the maximum likelihood estimators of the model parameters of a sequential k-out-of-n system are derived under order restrictions. Special attention is paid to the simultaneous maximum likelihood estimation of the model parameters and the distribution parameters for a flexible location-scale family. Furthermore, order restricted hypothesis tests are considered for making the decision whether the usual k-out-of-n model or the general sequential k-out-of-n model is appropriate for a given data.
Subfields: Software, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1b5e5872a283e41c777cd8093708a9905790b54e
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In Statistics and Probability
Citations in the corpus
- Parameter estimation from load-sharing system data using the expectation–maximization algorithm (2013, background): “…Phoenix (1978, 1982), Ross (1984), Durham et al. (1989), Schechner (1984), Lee et al. (1995), Hollander and Peña (1995), Durham et al. (1997), Mettas and Vassiliou (2004), Amari et al. (2008), Kvam and Peña (2005), Amari and Bergman (2008), Balakrishnan et al. (2008), and Amari and Pham (2009).”