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Updated: Jun 5, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Estimating the probability of rare events: addressing zero failure data.

John Quigley¹, Matthew Revie

¹Department of Management Science, University of Strathclyde,Glasgow G1 1QE, Scotland. j.quigley@strath.ac.uk.

Risk Analysis : an Official Publication of the Society for Risk Analysis

|January 15, 2011

Summary

This study introduces a novel minimax inference method for estimating event probabilities, especially when no events occur. The proposed approach offers a risk-averse procedure and approximates the maximum likelihood estimate for rare events.

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Last Updated: Jun 5, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

Statistics
Probability Theory
Inferential Statistics

Background:

Traditional statistical methods yield a zero probability estimate when no events are observed.
Existing alternative methods for zero-event data are often ad hoc and violate statistical principles.
Data-dependent inference decisions complicate the assessment of estimation procedure benefits.

Purpose of the Study:

To propose a novel inferential procedure for estimating event probabilities, particularly for rare events or zero-event data.
To develop a risk-averse estimation method that adheres to fundamental statistical principles.
To compare the proposed method with existing approaches, including the maximum likelihood estimate (MLE).

Main Methods:

Utilizing minimax inference on the probability of future samples realizing no more events than observed.
Applying the method to situations with zero observed events, while also applicable to non-zero data.
Comparing the proposed estimator with the MLE and standard inference approaches for zero-event data.

Main Results:

The minimax procedure provides a risk-averse approach for zero-event data.
The method closely approximates the MLE for non-zero event data.
The proposed estimator for zero events can be approximated by 1/(2.5n), where n is the number of trials.

Conclusions:

The proposed minimax inference offers a statistically sound and risk-averse alternative for estimating event probabilities, especially with zero observed events.
This approach outperforms standard methods that are often unduly pessimistic in zero-event scenarios.
The method provides a valuable tool for inference on rare events and situations with limited data.