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High dimensional semiparametric latent graphical model for mixed data

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  • Jianqing Fan
  • Han Liu
  • Yang Ning
  • Hui Zou

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  • Jianqing Fan & Han Liu & Yang Ning & Hui Zou, 2017. "High dimensional semiparametric latent graphical model for mixed data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 405-421, March.
    • Handle: RePEc:bla:jorssb:v:79:y:2017:i:2:p:405-421
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    File URL: http://hdl.handle.net/10.1111/rssb.12168
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    References listed on IDEAS

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    1. Fellinghauer, Bernd & Bühlmann, Peter & Ryffel, Martin & von Rhein, Michael & Reinhardt, Jan D., 2013. "Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 132-152.
    2. Shizhe Chen & Daniela M. Witten & Ali Shojaie, 2015. "Selection and estimation for mixed graphical models," Biometrika, Biometrika Trust, vol. 102(1), pages 47-64.
    3. 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.
    4. Fang Han & Wei Pan, 2012. "A Composite Likelihood Approach to Latent Multivariate Gaussian Modeling of SNP Data with Application to Genetic Association Testing," Biometrics, The International Biometric Society, vol. 68(1), pages 307-315, March.
    5. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    6. 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.
    7. Yingying Fan & Cheng Yong Tang, 2013. "Tuning parameter selection in high dimensional penalized likelihood," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 531-552, June.
    8. Teng Zhang & Hui Zou, 2014. "Sparse precision matrix estimation via lasso penalized D-trace loss," Biometrika, Biometrika Trust, vol. 101(1), pages 103-120.
    9. Anders Skrondal & Sophia Rabe‐Hesketh, 2007. "Latent Variable Modelling: A Survey," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 712-745, December.
    10. NESTEROV, Yu., 2005. "Smooth minimization of non-smooth functions," LIDAM Reprints CORE 1819, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    11. 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. Yutong Liu & Toni Darville & Xiaojing Zheng & Quefeng Li, 2023. "Decomposition of variation of mixed variables by a latent mixed Gaussian copula model," Biometrics, The International Biometric Society, vol. 79(2), pages 1187-1200, June.
    2. Yue Zhao & Ingrid Van Keilegom & Shanshan Ding, 2022. "Envelopes for censored quantile regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1562-1585, December.
    3. Xie, Zilong & Chen, Yunxiao & von Davier, Matthias & Weng, Haolei, 2023. "Variable selection in latent regression IRT models via knockoffs: an application to international large-scale assessment in education," LSE Research Online Documents on Economics 120812, London School of Economics and Political Science, LSE Library.
    4. Fan, Xinyan & Zhang, Qingzhao & Ma, Shuangge & Fang, Kuangnan, 2021. "Conditional score matching for high-dimensional partial graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    5. Kevin H. Lee & Qian Chen & Wayne S. DeSarbo & Lingzhou Xue, 2022. "Estimating Finite Mixtures of Ordinal Graphical Models," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 83-106, March.
    6. Jing Ma, 2021. "Joint Microbial and Metabolomic Network Estimation with the Censored Gaussian Graphical Model," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(2), pages 351-372, July.
    7. Popovic, Gordana C. & Hui, Francis K.C. & Warton, David I., 2018. "A general algorithm for covariance modeling of discrete data," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 86-100.

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