Predicting Extreme Values in Regression Problems with Heavy-Tailed Inputs

Core Concepts

The core message of this paper is to develop a general framework for regression on extremes, where the goal is to build predictive functions that perform well in regions of the input space with unusually large values. Under appropriate regular variation assumptions regarding the joint distribution of the input and output variables, the authors show that an asymptotic notion of risk can be tailored to summarize predictive performance in extreme regions, and that minimization of an empirical version of this 'extreme risk' yields good generalization capacity.

Abstract

The paper proposes a framework for regression on extremes, where the goal is to build predictive functions that perform well in regions of the input space with unusually large values. The key ideas are:

Assumptions: The authors introduce a regular variation framework, where the joint distribution of the input and output variables satisfies certain tail behavior assumptions. This allows them to define an asymptotic notion of risk that captures predictive performance in extreme regions.

Algorithm: The authors propose an algorithm that minimizes an empirical version of the 'extreme risk', based on a fraction of the largest observations in the training data. This empirical risk minimization approach is shown to have good generalization guarantees.

Theoretical Analysis: The authors establish several key theoretical results:

They show that prediction functions that depend only on the angular component of the input (i.e., the direction but not the magnitude) can optimally minimize the asymptotic extreme risk.
They provide non-asymptotic bounds on the excess of asymptotic risk for the empirical risk minimizer, demonstrating its near-optimality.

Numerical Experiments: The authors provide numerical results on both simulated and real data, supporting the relevance of their approach for regression problems with heavy-tailed inputs.

The paper provides a comprehensive framework for addressing regression problems where accurate prediction in extreme regions of the input space is crucial, with strong theoretical and empirical justification.

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Key Insights Distilled From

On Regression in Extreme Regions

by Nath... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2303.03084.pdf

Deeper Inquiries

How can the proposed framework be extended to handle unbounded target variables

To extend the proposed framework to handle unbounded target variables, one approach could be to consider a transformation of the target variable that ensures boundedness while preserving the relationship with the original variable. This transformation could involve scaling, normalization, or applying a suitable function to constrain the target variable within a specific range. By incorporating this transformation into the modeling process, the framework can effectively handle unbounded target variables without compromising the integrity of the analysis. Additionally, robust statistical techniques such as robust regression or quantile regression can be employed to account for outliers and extreme values in the target variable, ensuring the stability and reliability of the predictions.

What are the implications of the regular variation assumptions in practical applications, and how can they be verified or relaxed

The regular variation assumptions play a crucial role in practical applications, particularly in scenarios where extreme events or outliers have a significant impact on the overall analysis. These assumptions provide a flexible and robust framework for modeling heavy-tailed distributions and capturing the tail behavior of random variables. In practical applications such as risk analysis, anomaly detection, and environmental studies, the regular variation assumptions help in identifying and analyzing extreme events that may have rare but critical consequences.
To verify the regular variation assumptions, various statistical tests and diagnostic tools can be employed, such as tail index estimation, goodness-of-fit tests for heavy-tailed distributions, and visual inspection of tail behavior. Sensitivity analysis can also be conducted to assess the impact of deviations from the regular variation assumptions on the results.
In cases where strict adherence to the regular variation assumptions is challenging, relaxation strategies can be implemented by considering alternative heavy-tailed distributions or incorporating robust modeling techniques that are less sensitive to deviations from the assumptions. By carefully evaluating the implications of the regular variation assumptions and adapting them to the specific characteristics of the data, practitioners can enhance the reliability and applicability of the analysis in practical settings.

Can the ideas developed in this paper be applied to other supervised learning problems beyond regression, such as classification or ranking on extremes

The ideas developed in the paper on regression in extreme regions can be extended to other supervised learning problems beyond regression, such as classification or ranking on extremes. By adapting the framework to accommodate different types of prediction tasks, practitioners can address challenges related to extreme observations, rare events, and heavy-tailed distributions in various domains.
For classification on extremes, the framework can be modified to focus on predicting rare or extreme classes by leveraging regular variation assumptions and angular prediction functions. By considering the angular information and tail behavior of the data, classifiers can be designed to effectively handle imbalanced datasets and extreme class distributions.
In the context of ranking on extremes, the framework can be applied to scenarios where the goal is to rank observations based on their extremeness or outlier status. By incorporating regular variation assumptions and developing ranking algorithms that prioritize extreme observations, practitioners can identify and prioritize critical data points in ranking tasks.
Overall, the concepts and methodologies introduced in the paper can be adapted and extended to a wide range of supervised learning problems, providing valuable insights and strategies for handling extreme regions and heavy-tailed data distributions in diverse applications.