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Titel:

Robust and sparse Gaussian graphical modelling under cell-wise contamination

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Katayama, Shota; Fujisawa, Hironori; Drton, Mathias
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 con...     »
Stichworte:
cell‐wise contamination, Gaussian graphical modelling, precision matrix, robust inference, sparsity
Dewey Dezimalklassifikation:
510 Mathematik
Zeitschriftentitel:
Stat
Jahr:
2018
Band / Volume:
7
Jahr / Monat:
2018-02
Quartal:
1. Quartal
Monat:
Feb
Heft / Issue:
1
Seitenangaben Beitrag:
e181
Volltext / DOI:
doi:10.1002/sta4.181
Verlag / Institution:
Wiley
E-ISSN:
2049-1573
Eingereicht (bei Zeitschrift):
24.01.2018
Angenommen (von Zeitschrift):
13.02.2018
Publikationsdatum:
13.02.2018
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