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A Note on the Inverse Birthday Problem With Applications

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  • Wen-Han Hwang
  • Richard Huggins
  • Lu-Fang Chen

Abstract

The classical birthday problem considers the probability that at least two people in a group of size N share the same birthday. The inverse birthday problem considers the estimation of the size N of a group given the number of different birthdays in the group. In practice, this problem is analogous to estimating the size of a population from occurrence data only. The inverse problem can be solved via two simple approaches including the method of moments for a multinominal model and the maximum likelihood estimate of a Poisson model, which we present in this study. We investigate properties of both methods and show that they can yield asymptotically equivalent Wald-type interval estimators. Moreover, we show that these methods estimate a lower bound for the population size when birth rates are nonhomogenous or individuals in the population are aggregated. A simulation study was conducted to evaluate the performance of the point estimates arising from the two approaches and to compare the performance of seven interval estimators, including likelihood ratio and log-transformation methods. We illustrate the utility of these methods by estimating: (1) the abundance of tree species over a 50-hectare forest plot, (2) the number of Chlamydia infections when only the number of different birthdays of the patients is known, and (3) the number of rainy days when the number of rainy weeks is known. Supplementary materials for this article are available online.

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  • Wen-Han Hwang & Richard Huggins & Lu-Fang Chen, 2017. "A Note on the Inverse Birthday Problem With Applications," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 191-201, July.
    • Handle: RePEc:taf:amstat:v:71:y:2017:i:3:p:191-201
    DOI: 10.1080/00031305.2016.1255657
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    References listed on IDEAS

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    1. Müller, Christine H. & Huggins, Richard & Hwang, Wen-Han, 2011. "Consistent estimation of species abundance from a presence-absence map," Statistics & Probability Letters, Elsevier, vol. 81(9), pages 1449-1457, September.
    2. Lucio Barabesi & Marzia Marcheselli, 2011. "Parameter estimation in the classical occupancy model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 305-327, August.
    3. Shirley Pledger, 2000. "Unified Maximum Likelihood Estimates for Closed Capture–Recapture Models Using Mixtures," Biometrics, The International Biometric Society, vol. 56(2), pages 434-442, June.
    4. Dorazio, Robert M. & Royle, J. Andrew, 2005. "Estimating Size and Composition of Biological Communities by Modeling the Occurrence of Species," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 389-398, June.
    5. Williamson, Patricia Pepple & Mays, Darcy P. & Abay Asmerom, Ghidewon & Yang, Yingying, 2009. "Revisiting the Classical Occupancy Problem," The American Statistician, American Statistical Association, vol. 63(4), pages 356-360.
    6. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
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    1. Wen‐Han Hwang & Richard Huggins & Jakub Stoklosa, 2022. "A model for analyzing clustered occurrence data," Biometrics, The International Biometric Society, vol. 78(2), pages 598-611, June.

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