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@ARTICLE{Gbler:271116,
      author       = {Göbler, Konstantin and Drton, Mathias and Mukherjee, Sach
                      and Miloschewski, Anne},
      title        = {{H}igh-dimensional undirected graphical models for
                      arbitrary mixed data},
      journal      = {Electronic journal of statistics},
      volume       = {18},
      number       = {1},
      issn         = {1935-7524},
      address      = {Ithaca, NY},
      publisher    = {Cornell University Library},
      reportid     = {DZNE-2024-00984},
      pages        = {2339 - 2404},
      year         = {2024},
      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 modeled
                      as transformations of underlying jointly Gaussian variables,
                      represent a useful approach. Recent advances have shown how
                      the binary-continuous case can be tackled, but the general
                      mixed variable type regime remains challenging. In this
                      work, we make the simple but useful observation that
                      classical ideas concerning polychoric and polyserial
                      correlations can be leveraged in a latent Gaussian copula
                      framework. Building on this observation, we propose a
                      flexible and scalable methodology for data with variables of
                      entirely general mixed type. We study the key properties of
                      the approaches theoretically and empirically.},
      cin          = {AG Mukherjee},
      ddc          = {310},
      cid          = {I:(DE-2719)1013030},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1214/24-EJS2254},
      url          = {https://pub.dzne.de/record/271116},
}