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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Estimation of Tail Probabilities by Repeated Augmented Reality.

Benjamin Kedem¹, Saumyadipta Pyne^2,3

¹Department of Mathematics, Institute for Systems Research, University of Maryland, College Park, MD USA.

Journal of Statistical Theory and Practice

|January 26, 2021

Summary

Synthetic data augmentation improves the precise estimation of rare event tail probabilities from real-world observations. This novel iterative approach enhances pattern detection and provides valuable insights for complex problems.

Keywords:

B-curve Density ratio model Iterative process Repeated out of sample fusion Residential radon Upper bounds

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Area of Science:

Statistics
Data Science
Computational Methods

Background:

Estimating tail probabilities of rare events is crucial for risk assessment.
Real-world data often lacks sufficient observations for rare events.
Synthetic data offers a potential solution to augment limited datasets.

Purpose of the Study:

To develop a method for precise estimation of tail probabilities for rare events.
To investigate the utility of synthetic data augmentation in this context.
To introduce a novel iterative process for probability approximation.

Main Methods:

Augmenting real data with a large number of computer-generated synthetic samples.
Employing a novel iterative process to create subsequences from augmented data.
Approximating tail probabilities using these generated subsequences.

Main Results:

The proposed method, utilizing synthetic data augmentation, yields precise estimates.
The iterative process effectively approximates the tail probabilities of rare events.
Enhanced pattern detection was observed in the augmented datasets.

Conclusions:

Synthetic data augmentation is a viable and effective strategy for estimating rare event tail probabilities.
The novel iterative method provides accurate and precise estimations.
This approach offers valuable insights for problems involving rare events in moderately large datasets.