A framework for statistical modelling of the extremes of longitudinal data, applied to elite swimming

TL;DR

This paper introduces a novel statistical framework based on extreme value theory for analyzing the tails of irregularly sampled longitudinal data, with a case study on elite swimmers' performance.

Contribution

It develops a flexible Bayesian methodology to model extreme values in longitudinal data, capturing dependence and temporal changes, which was not previously addressed.

Findings

Able to infer individual swimmer career probabilities

Captured both dependence and independence in extreme events

Provided insights into record-breaking likelihoods

Abstract

We develop methods, based on extreme value theory, for analysing observations in the tails of longitudinal data, i.e., a data set consisting of a large number of short time series, which are typically irregularly and non-simultaneously sampled, yet have some commonality in the structure of each series and exhibit independence between time series. Extreme value theory has not been considered previously for the unique features of longitudinal data. Across time series the data are assumed to follow a common generalised Pareto distribution, above a high threshold. To account for temporal dependence of such data we require a model to describe (i) the variation between the different time series properties, (ii) the changes in distribution over time, and (iii) the temporal dependence within each series. Our methodology has the flexibility to capture both asymptotic dependence and asymptotic independence, with this characteristic determined by the data. Bayesian inference is used given the need for inference of parameters that are unique to each time series. Our novel methodology is illustrated through the analysis of data from elite swimmers in the men's 100m breaststroke. Unlike previous analyses of personal-best data in this event, we are able to make inference about the careers of individual swimmers - such as the probability an individual will break the world record or swim the fastest time next year.