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A likelihood ratio test for completed sampling in population size estimation studies.

Alessio Farcomeni1

  • 1Department of Economics and Finance, University of Rome "Tor Vergata", Rome, Italy.

Biometrical Journal. Biometrische Zeitschrift
|September 14, 2022
PubMed
Summary
This summary is machine-generated.

A new likelihood ratio test helps determine if sampling is complete for population size estimation. This method assesses if the number of unsampled subjects meets a specific threshold, improving study accuracy.

Keywords:
ascertainment biascapture-recapturechi-bar squaredconstrained optimization

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Area of Science:

  • Biostatistics
  • Ecological Statistics
  • Epidemiological Methods

Background:

  • Accurate population size estimation relies on complete sampling.
  • Assessing sampling completeness is crucial for reliable closed population studies.
  • Existing methods may lack robustness or require specific model assumptions.

Purpose of the Study:

  • To introduce a novel likelihood ratio test (LRT) for assessing sampling completeness in closed population size estimation.
  • To evaluate if the expected number of unsampled individuals falls below a predefined threshold.
  • To provide a statistically rigorous and broadly applicable method for sampling adequacy assessment.

Main Methods:

  • Development of a likelihood ratio test statistic.
  • Analysis of the nonstandard distribution of the LRT statistic under the null hypothesis.
  • Approximation and tabulation of critical values independent of model specification.

Main Results:

  • The proposed LRT provides a robust measure of sampling completeness.
  • Critical values are easily approximated and do not require model-specific parameters.
  • The method's utility is demonstrated through simulation and real-world case studies.

Conclusions:

  • The likelihood ratio test offers a valuable tool for validating sampling efforts in population studies.
  • This approach enhances the reliability of closed population size estimates.
  • The test is applicable across various fields, including ecological and epidemiological research, addressing potential biases like ascertainment bias.