# Change point detection for graphical models in the presence of missing values

## TL;DR

This paper introduces new methods for detecting change points in high-dimensional graphical models with missing data, comparing imputation strategies and model selection adjustments through simulations and real environmental data analysis.

## Contribution

It presents novel estimation techniques for change points in covariance structures with missing values, including adapted model selection and implementation in an R package.

## Key findings

- Imputation methods impact change point detection accuracy
- Proposed methods outperform existing approaches in simulations
- Effective in environmental time series analysis

## Abstract

We propose estimation methods for change points in high-dimensional covariance structures with an emphasis on challenging scenarios with missing values. We advocate three imputation like methods and investigate their implications on common losses used for change point detection. We also discuss how model selection methods have to be adapted to the setting of incomplete data. The methods are compared in a simulation study and applied to a time series from an environmental monitoring system. An implementation of our proposals within the R-package hdcd is available via the Supplementary materials.