Change point detection for graphical models in the presence of missing values
This work addresses a specific challenge in statistical analysis for domains like environmental monitoring, but it is incremental as it builds on existing change point detection methods by handling missing data.
The paper tackles the problem of detecting change points in high-dimensional graphical models when data contain missing values, proposing three imputation-based methods and adapting model selection for incomplete data, with results validated through simulations and an environmental monitoring application.
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.