Modeling Extreme Events in the Presence of Inlier: A Mixture Approach

TL;DR

This paper proposes a mixture model using the generalized Pareto distribution to better capture extreme events and inliers, especially zeros, in environmental and life-testing data, improving tail approximation accuracy.

Contribution

It introduces a novel framework that models inliers and extremes simultaneously, addressing threshold uncertainty and outperforming traditional methods that neglect inliers.

Findings

Model effectively captures zero inliers and extreme values.

Simulation and real data show improved tail estimation accuracy.

Maximum likelihood estimation ensures precise parameter fitting.

Abstract

In many random phenomena, such as life-testing experiments and environmental data (like rainfall data), there are often positive values and an excess of zeros, which create modeling challenges. In life testing, immediate failures result in zero lifetimes, often due to defects or poor quality, especially in electronics and clinical trials. These failures, called zero inliers, are difficult to model using standard approaches. When studying extreme values in the above scenarios, a key issue is selecting an appropriate threshold for accurate tail approximation of the population using asymptotic models. While some extreme value mixture models address threshold estimation and tail approximation, conventional parametric and non-parametric bulk and generalised Pareto distribution (GPD) approaches often neglect inliers, leading to suboptimal results. This paper introduces a framework for modeling extreme events and inliers using the GPD, addressing threshold uncertainty and effectively capturing inliers at zero. The model's parameters are estimated using the maximum likelihood estimation (MLE) method, ensuring optimal precision. Through simulation studies and real-world applications, we demonstrate that the proposed model significantly outperforms the traditional methods, which typically neglect inliers at the origin.