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Title:

High-dimensional undirected graphical models for arbitrary mixed data

Document type:
Zeitschriftenaufsatz
Author(s):
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...     »
Keywords:
Generalized correlation, high-dimensional statistics, latent Gaussian copula, mixed data, polychoric/polyserial correlation, undirected graphical models
Dewey Decimal Classification:
510 Mathematik
Journal title:
Electronic Journal of Statistics
Year:
2024
Journal volume:
18
Year / month:
2024-06
Quarter:
2. Quartal
Month:
Jun
Journal issue:
1
Pages contribution:
2339–2404
Language:
en
Fulltext / DOI:
doi:10.1214/24-ejs2254
Publisher:
Institute of Mathematical Statistics
E-ISSN:
1935-7524
Status:
Erstveröffentlichung
Submitted:
01.09.2022
Date of publication:
01.01.2024
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
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