Seminar in applied mathematics and statistics
SPEAKER: Shota Katayama (Tokyo Institute of Technology).
TITLE: Robust and sparse Gaussian graphical modelling under cell-wise contamination.
ABSTRACT: Graphical modelling explores dependences among a collection of variables by inferring a graph that encodes pairwise conditional independences. For jointly Gaussian variables, this translates into detecting the support of the precision matrix. Many modern applications feature high-dimensional and contaminated data that complicate this task. In particular, traditional robust methods that down-weight entire observation vectors are often inappropriate as high-dimensional data may feature partial contamination in many observations. We tackle this problem by giving a robust method for sparse precision matrix estimation based on the gamma divergence give by Fujisawa and Eguchi (2008, JMVA) under a cell-wise contamination model. This talk first introduces popular methods that can obtain sparse Gaussian graphical model, the concept of cell-wise contamination and gamma-divergence briefly, then provides our procedure. Finally, we show simulation studies and real data analyses for low and high dimensional data. This demonstrates that our procedure outperforms existing methods especially for highly contaminated data.
Tea and chocolate will be served in room 04.4.19 after the seminar.
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Upcoming events:
Wednesday, December 5 at 14.15: Rafael Serrano
Friday, December 14 at 14.15: Moritz M. Schauer
Friday, February 8, 2019, at 14.15: Massimiliano Tamborrino
Friday, February 15, 2019 at 14.15: Irene Tubikanec