Order restricted inference for sequential k-out-of-n systems

  • Authors:

  • N. Balakrishnan;E. Beutner;U. Kamps

  • Affiliations:

  • Department of Mathematics and Statistics, McMaster University, Hamilton, 1280 Main Street West Hamilton, Ontario L8S 4K1, Canada;Institute of Statistics, RWTH Aachen University, 52056 Aachen, Germany;Institute of Statistics, RWTH Aachen University, 52056 Aachen, Germany

  • Venue:
  • Journal of Multivariate Analysis
  • Year:
  • 2008

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Abstract

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.