Multinomial group testing models with incomplete identification

Shaul K. Bar-Lev, Wolfgang Stadje, Frank A. van der Duyn Schouten

Research output: Contribution to journalArticlepeer-review


We study reliable multinomial probabilistic group testing models with incomplete identification. We assume that every of the pooled items has none or some of k attributes, one of them causing contamination. Any group possessing this latter attribute is discarded, while the others are collected and separated according to the attributes that were found in them. The objective is to choose an optimal group size for pooled screening so as to collect prespecified numbers of items of the various types with minimum testing expenditures. We derive exact results for the underlying distributions of the stopping times, enabling us to find optimal procedures by numerical methods.

Original languageEnglish
Pages (from-to)384-401
Number of pages18
JournalJournal of Statistical Planning and Inference
Issue number2
StatePublished - 1 Dec 2005

Bibliographical note

Funding Information:
S.K. Bar-Lev was partially supported by NWO Grant no. B61-493. The authors would like to thank Andreas Löpker for his assistance in the numerical part of this study and the referees for many valuable comments, references and suggestions which considerably improved the paper.


  • Group testing
  • Multinomial trials
  • Optimal group size
  • Optimal truncation
  • Stopping rule

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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