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Related Experiment Video

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Bias Correction in Estimating Proportions by Pooled Testing.

Graham Hepworth¹, Brad J Biggerstaff²

¹School of Mathematics and Statistics, The University of Melbourne, Victoria 3010, Australia, hepworth@unimelb.edu.au.

Journal of Agricultural, Biological, and Environmental Statistics

|January 15, 2019

Summary

This summary is machine-generated.

This study introduces a new, nearly unbiased estimator for pooled testing, improving upon existing methods for estimating proportions. The novel approach offers superior performance in plant disease and virus prevalence assessments.

Keywords:

Bias correction Estimation of proportions Group testing Pooled testing Virus prevalence

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Area of Science:

Statistics
Biostatistics
Epidemiology

Background:

Maximum Likelihood Estimation (MLE) for pooled testing is known to be biased.
Existing bias correction methods have limitations.
Pooled testing is crucial for disease and virus prevalence estimation.

Purpose of the Study:

To propose a novel, bias-corrected estimator for pooled testing.
To provide an efficient computational method for the new estimator.
To evaluate the performance of the proposed estimator against existing methods.

Main Methods:

Utilizing Firth's bias correction method (1993) by modifying the score function.
Developing a Newton-Raphson iterative formula for computation.
Comparing the new estimator with existing bias correction techniques.

Main Results:

The proposed estimator demonstrates near-unbiasedness across various scenarios.
The new estimator outperforms previously available methods.
For equal pool sizes, it is equivalent to Burrows' estimator (1987).

Conclusions:

The novel estimator offers a significant improvement for bias correction in pooled testing.
The method is applicable to real-world problems like plant disease assessment and virus prevalence studies.
The computational formula facilitates practical implementation.