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A sparse ising model with covariates

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  • Jie Cheng
  • Elizaveta Levina
  • Pei Wang
  • Ji Zhu

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  • Jie Cheng & Elizaveta Levina & Pei Wang & Ji Zhu, 2014. "A sparse ising model with covariates," Biometrics, The International Biometric Society, vol. 70(4), pages 943-953, December.
    • Handle: RePEc:bla:biomet:v:70:y:2014:i:4:p:943-953
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    File URL: http://hdl.handle.net/10.1111/biom.12202
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    References listed on IDEAS

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    1. Peng, Jie & Wang, Pei & Zhou, Nengfeng & Zhu, Ji, 2009. "Partial Correlation Estimation by Joint Sparse Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 735-746.
    2. T. Tony Cai & Hongzhe Li & Weidong Liu & Jichun Xie, 2013. "Covariate-adjusted precision matrix estimation with an application in genetical genomics," Biometrika, Biometrika Trust, vol. 100(1), pages 139-156.
    3. Pei Wang & Dennis L. Chao & Li Hsu, 2011. "Learning Oncogenic Pathways from Binary Genomic Instability Data," Biometrics, The International Biometric Society, vol. 67(1), pages 164-173, March.
    4. Lam, Clifford & Fan, Jianqing, 2009. "Sparsistency and rates of convergence in large covariance matrix estimation," LSE Research Online Documents on Economics 31540, London School of Economics and Political Science, LSE Library.
    5. Cai, Tony & Liu, Weidong & Luo, Xi, 2011. "A Constrained â„“1 Minimization Approach to Sparse Precision Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 594-607.
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    Cited by:

    1. Jingnan Zhang & Chengye Li & Junhui Wang, 2023. "A stochastic block Ising model for multi‐layer networks with inter‐layer dependence," Biometrics, The International Biometric Society, vol. 79(4), pages 3564-3573, December.
    2. Shanghong Xie & Xiang Li & Peter McColgan & Rachael I. Scahill & Donglin Zeng & Yuanjia Wang, 2020. "Identifying disease‐associated biomarker network features through conditional graphical model," Biometrics, The International Biometric Society, vol. 76(3), pages 995-1006, September.
    3. Paweł Teisseyre & Robert A. Kłopotek & Jan Mielniczuk, 2016. "Random Subspace Method for high-dimensional regression with the R package regRSM," Computational Statistics, Springer, vol. 31(3), pages 943-972, September.
    4. Forzani, Liliana & Rodriguez, Daniela & Smucler, Ezequiel & Sued, Mariela, 2019. "Sufficient dimension reduction and prediction in regression: Asymptotic results," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 339-349.
    5. Rodrigo García Arancibia & Ignacio Girela, 2021. "Conditional Associations of Multidimensional Poverty Indicators in Argentina: A Graphical Representation," Asociación Argentina de Economía Política: Working Papers 4478, Asociación Argentina de Economía Política.
    6. Rodrigo García Arancibia & Ignacio Girela, 2024. "Graphical Representation of Multidimensional Poverty: Insights for Index Construction and Policy Making," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 172(2), pages 595-634, March.
    7. Yang Ni & Veerabhadran Baladandayuthapani & Marina Vannucci & Francesco C. Stingo, 2022. "Bayesian graphical models for modern biological applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 197-225, June.
    8. Xiao Guo & Hai Zhang, 2020. "Sparse directed acyclic graphs incorporating the covariates," Statistical Papers, Springer, vol. 61(5), pages 2119-2148, October.

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