Graphical Influence Diagnostics for Changepoint Models

TL;DR

This paper introduces a new graphical influence diagnostic framework for changepoint models, enabling practitioners to detect influential observations and assess model stability in ordered data analysis.

Contribution

It develops a novel graphical diagnostic approach for changepoint models, addressing the lack of influence diagnostics and enhancing interpretability of model instabilities.

Findings

Diagnostic plots reveal influential observations affecting changepoint segmentation.

Application to well-log data demonstrates practical utility in uncovering hidden data features.

Framework helps identify and visualize instabilities in changepoint analysis.

Abstract

Changepoint models enjoy a wide appeal in a variety of disciplines to model the heterogeneity of ordered data. Graphical influence diagnostics to characterize the influence of single observations on changepoint models are, however, lacking. We address this gap by developing a framework for investigating instabilities in changepoint segmentations and assessing the influence of single observations on various outputs of a changepoint analysis. We construct graphical diagnostic plots that allow practitioners to assess whether instabilities occur; how and where they occur; and to detect influential individual observations triggering instability. We analyze well-log data to illustrate how such influence diagnostic plots can be used in practice to reveal features of the data that may otherwise remain hidden.