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Titel:

High-dimensional undirected graphical models for arbitrary mixed data

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Göbler, Konstantin; Drton, Mathias; Mukherjee, Sach; Miloschewski, Anne
Abstract:
Graphical models are an important tool in exploring relationships between variables in complex, multivariate data. Methods for learning such graphical models are well-developed in the case where all variables are either continuous or discrete, including in high dimensions. However, in many applications, data span variables of different types (e.g., continuous, count, binary, ordinal, etc.), whose principled joint analysis is nontrivial. Latent Gaussian copula models, in which all variables are m...     »
Stichworte:
Generalized correlation, high-dimensional statistics, latent Gaussian copula, mixed data, polychoric/polyserial correlation, undirected graphical models
Dewey Dezimalklassifikation:
510 Mathematik
Zeitschriftentitel:
Electronic Journal of Statistics
Jahr:
2024
Band / Volume:
18
Jahr / Monat:
2024-06
Quartal:
2. Quartal
Monat:
Jun
Heft / Issue:
1
Seitenangaben Beitrag:
2339–2404
Sprache:
en
Volltext / DOI:
doi:10.1214/24-ejs2254
Verlag / Institution:
Institute of Mathematical Statistics
E-ISSN:
1935-7524
Status:
Erstveröffentlichung
Eingereicht (bei Zeitschrift):
01.09.2022
Publikationsdatum:
01.01.2024
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik
Format:
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