A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions (English)
- New search for: Samanta, Srijata
- New search for: Khare, Kshitij
- New search for: Michailidis, George
- Further information on Michailidis, George:
- https://orcid.org/http://orcid.org/0000-0002-3676-1739
- New search for: Samanta, Srijata
- New search for: Khare, Kshitij
- New search for: Michailidis, George
- Further information on Michailidis, George:
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In:
Statistics and Computing
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32
, 3
;
2022
- Article (Journal) / Electronic Resource
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Title:A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions
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Additional title:Stat Comput
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Contributors:
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Published in:Statistics and Computing ; 32, 3
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Publisher:
- New search for: Springer US
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Place of publication:New York
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Publication date:2022-06-01
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ISSN:
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DOI:
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Type of media:Article (Journal)
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Type of material:Electronic Resource
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Language:English
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Keywords:
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Source:
Table of contents – Volume 32, Issue 3
The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.
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High-dimensional regression with potential prior information on variable importanceStokell, Benjamin G. / Shah, Rajen D. et al. | 2022
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Importance conditional sampling for Pitman–Yor mixturesCanale, Antonio / Corradin, Riccardo / Nipoti, Bernardo et al. | 2022
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A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensionsSamanta, Srijata / Khare, Kshitij / Michailidis, George et al. | 2022
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The node-wise Pseudo-marginal method: model selection with spatial dependence on latent graphsThesingarajah, Denishrouf / Johansen, Adam M. et al. | 2022
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Sklar’s Omega: A Gaussian copula-based framework for assessing agreementHughes, John et al. | 2022
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Optimally adaptive Bayesian spectral density estimation for stationary and nonstationary processesJames, Nick / Menzies, Max et al. | 2022
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Multilevel estimation of normalization constants using ensemble Kalman–Bucy filtersRuzayqat, Hamza / Chada, Neil K. / Jasra, Ajay et al. | 2022
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Distributional anchor regressionKook, Lucas / Sick, Beate / Bühlmann, Peter et al. | 2022
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Parsimonious hidden Markov models for matrix-variate longitudinal dataTomarchio, Salvatore D. / Punzo, Antonio / Maruotti, Antonello et al. | 2022
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Rule-based Bayesian regressionBotsas, Themistoklis / Mason, Lachlan R. / Pan, Indranil et al. | 2022
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Complexity of zigzag sampling algorithm for strongly log-concave distributionsLu, Jianfeng / Wang, Lihan et al. | 2022
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Accelerated parallel non-conjugate sampling for Bayesian non-parametric modelsZhang, Michael Minyi / Williamson, Sinead A. / Pérez-Cruz, Fernando et al. | 2022
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Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlovan den Boom, Willem / Jasra, Ajay / De Iorio, Maria / Beskos, Alexandros / Eriksson, Johan G. et al. | 2022
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Particle gradient descent model for point process generationBrochard, Antoine / Błaszczyszyn, Bartłomiej / Zhang, Sixin / Mallat, Stéphane et al. | 2022
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Co-clustering of evolving count matrices with the dynamic latent block model: application to pharmacovigilanceMarchello, Giulia / Fresse, Audrey / Corneli, Marco / Bouveyron, Charles et al. | 2022
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Joint latent space models for ranking data and social networkGu, Jiaqi / Yu, Philip L. H. et al. | 2022
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A comparison of likelihood-free methods with and without summary statisticsDrovandi, Christopher / Frazier, David T. et al. | 2022
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Selecting the derivative of a functional covariate in scalar-on-function regressionHooker, Giles / Shang, Han Lin et al. | 2022
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Biclustering via structured regularized matrix decompositionZhong, Yan / Huang, Jianhua Z. et al. | 2022