Change-Point Detection For Graphical Models In The Presence Of Missing Values
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS(2021)
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 online .
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Key words
Covariance estimation, Graphical Lasso, High-dimensional models, Incomplete data, Precision matrix, Time-varying models
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