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Some limit results in estimation of proportion based on group testing

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Abstract

Group testing that tests groups with k experimental units instead of individuals has a long history and is very useful for estimating small proportion p under certain conditions. There are two sampling schemes for implementing group testing, one is Binomial sampling in which the number of groups n is predetermined, and another one is negative binomial sampling where the total number n of groups with a trait is predetermined. Many estimators including both the frequentist and Bayesian estimator have been proposed. The performance of all these estimators certainly depends on n and k. For the Bayesian estimators it also depends on the hyper-parameter \(\beta \) in the prior distribution \(Beta(1, \beta )\) of the proportion p. The present article studies the limits of all these estimators when n, or k, or \(\beta \) goes to infinity. The obtained results may be helpful with selecting nk, and \(\beta \).

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References

  • Abramowitz M, Stegun IA (1960) Handbook of mathematical functions with formulas, graphs, and mathematical tables. Bureau of National Standards, Washingto

    MATH  Google Scholar 

  • Biggerstaff B (2008) Confidence intervals for the difference of two proportions estimated from pooled samples. J Agric Biol Environ Stat 13:478–496

    Article  MathSciNet  Google Scholar 

  • Bilder CR et al (2010) Informative retesting. J Am Stat Assoc 105:942–955

    Article  MathSciNet  Google Scholar 

  • Burrows PM (1987) Improved estimation of pathogen transmission rates by group testing. Phytopathology 77:363–365

    Article  Google Scholar 

  • Chang CL, Reeves WC (1962) Statistical estimation of virus infection rates in mosquito vector populations. Am J Hyg 75:377–391

    Google Scholar 

  • Chaubey Y, Li W (1995) Comparison between maximum likelihood and Bayes methods for estimation of binomial probability with sample compositing. J Off Stat 11:379–390

    Google Scholar 

  • Dorfman R (1943) The detection of defective members of large populations. Ann Math Stat 14:436–440

    Article  Google Scholar 

  • Gart JJ (1991) An application of score methodology: confidence intervals and tests of fit for one-hit curves. In: Rao CR, Chakraborty R (eds) Handbook of statistics, vol 8. Elsevier, Amsterdam, pp 395–406

    Google Scholar 

  • Gastwirth J (2000) The efficiency of pooling in the detection of rare mutations. Am J Hum Genet 67:1035–1039

    Article  Google Scholar 

  • Gibbs AJ, Gower J (1960) The use of a multipletransfer method in plant virus transmission studies—some statistical points arising in the analysis of results. Ann Appl Biol 48:75–83

    Article  Google Scholar 

  • Griffiths DA (1972) A further note on the probability of disease transmission. Biometrics 28:1133–1139

    Article  Google Scholar 

  • Haldane JBS (1945) On a method of estimating frequencies. Biometrika 33:222–225

    Article  MathSciNet  Google Scholar 

  • Hepworth G (2005) Confidence intervals for proportions estimated by group testing with groups of unequal size. J Agric Biol Environ Stat 10:478–497

    Article  Google Scholar 

  • Hepworth G (2013) Improved estimation of proportions using inverse binomial group testing. J Agric Biol Environ Stat 18:102–119

    Article  MathSciNet  Google Scholar 

  • Hepworth G, Watson JJ (2009) Debiased estimation of proportions in group testing. J R Stat Soc Ser C Appl Stat 58:105–121

    Article  MathSciNet  Google Scholar 

  • Huang K, Mi J (2015) An alternative Bayesian estimator of small proportion using binomial group testing. Int J Appl Math Stat 53(5):1–15

    MathSciNet  MATH  Google Scholar 

  • Huang K, Mi J (2018) Applications of likelihood ratio order in Bayesian inferences. Probab Eng Inf Sci. https://doi.org/10.1017/S026996481800027X

  • Hughes-Oliver JM (2006) Pooling experiments for blood screening and drug discover. In: Dean A, Lewis S (eds) Screening: methods for experimentation in industry drug discovery and genetics. Springer, New York

    Google Scholar 

  • Hughes-Oliver JM, Swallow WH (1994) A twos tage adaptive group-testing procedure for estimating small proportions. J Am Stat Assoc 89:982–993

    Article  Google Scholar 

  • Hung M, Swallow WH (1999) Robustness of group testing in the estimation of proportions. Biometrics 55:231–237

    Article  Google Scholar 

  • Jayanta K, Ghosh MD, Tapas S (2006) An introduction to Bayesian analysis theory and methods. Springer, Berlin

    MATH  Google Scholar 

  • Katholi R, Unnasch T (2006) Important experimental parameters for determining infection rates in arthropod vectors using pool screening approaches. Am J Trop Med Hyg 74:779–785

    Article  Google Scholar 

  • McCann M, Tebbs J (2007) Pairwise comparisons for proportions estimated by pooled testing. J Stat Plan Inference 137:1278–1290

    Article  MathSciNet  Google Scholar 

  • Peck C (2006) Going after BVD. Beef 42:34–44

    Google Scholar 

  • Pritchard N, Tebbs J (2011a) Estimating disease prevalence using inverse binomial pooled testing. J Agric Biol Environ Stat 16(1):70–87

    Article  MathSciNet  Google Scholar 

  • Pritchard N, Tebbs J (2011b) Bayesian inference for disease prevalence using negative binomial group testing. Biom J 53(1):40–56

    Article  MathSciNet  Google Scholar 

  • Remlinger K, Hughes-Oliver J, Young S, Lam R (2006) Statistical design of pools using optimal coverage and minimal collision. Teechnometrics 48:133–143

    Article  MathSciNet  Google Scholar 

  • Swallow WH (1985) Group testing for estimating infection rates and probabilities of disease transmission. Phytopathology 75(8):882–889

    Article  Google Scholar 

  • Tebbs J, Swallow WH (2003) Estimating ordered binomial proportions with the use of group testing. Biometrika 90:471–477

    Article  MathSciNet  Google Scholar 

  • Tebbs J, Bilder CR, Moser BK (2003) An empirical Bayes group-testing approach to estimating small proportions. Commun Stat Theory Methods 32:983–995

    Article  MathSciNet  Google Scholar 

  • Thompson KH (1962) Estimation of the proportion of vectors in a natural population of insects. Biometrics 18(4):568–578

    Article  Google Scholar 

Download references

Acknowledgements

The author would like to thank the Associate Editor and the anonymous reviewers for their valuable comments.

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Correspondence to Jie Mi.

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Mi, J. Some limit results in estimation of proportion based on group testing. Metrika 82, 1021–1038 (2019). https://doi.org/10.1007/s00184-019-00719-4

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