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

Malte Londschien; Solt Kovács; Peter Bühlmann

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arXiv · 1907.05409

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

Malte Londschien·Solt Kovács·Peter Bühlmann

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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.

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BibTeX RIS

Malte Londschien, Solt Kovács, Peter Bühlmann. 2019-07-11. Change point detection for graphical models in the presence of missing values. https://arxiv.org/abs/1907.05409

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