Pairwise sparse + low-rank models for variables of mixed type
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[1] Joachim Giesen,et al. Efficient Regularization Parameter Selection for Latent Variable Graphical Models via Bi-Level Optimization , 2019, IJCAI.
[2] C. Spearman. General intelligence Objectively Determined and Measured , 1904 .
[3] Trevor Hastie,et al. Learning the Structure of Mixed Graphical Models , 2015, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.
[4] J. Lafferty,et al. High-dimensional Ising model selection using ℓ1-regularized logistic regression , 2010, 1010.0311.
[5] Daphna Weinshall,et al. Online Learning in The Manifold of Low-Rank Matrices , 2010, NIPS.
[6] Jingchen Liu,et al. Robust Measurement via A Fused Latent and Graphical Item Response Theory Model , 2018, Psychometrika.
[7] R. Adamczak,et al. Quantitative estimates of the convergence of the empirical covariance matrix in log-concave ensembles , 2009, 0903.2323.
[8] M. Wedel,et al. Factor analysis with (mixed) observed and latent variables in the exponential family , 2001 .
[9] Bin Yu,et al. High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergence , 2008, 0811.3628.
[10] D. Bartholomew. Latent Variable Models And Factor Analysis , 1987 .
[11] Pablo A. Parrilo,et al. Rank-Sparsity Incoherence for Matrix Decomposition , 2009, SIAM J. Optim..
[12] A. Willsky,et al. Latent variable graphical model selection via convex optimization , 2010, 1008.1290.
[13] L. Ryan,et al. Latent Variable Models for Mixed Discrete and Continuous Outcomes , 1997 .
[14] N. Meinshausen,et al. High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.
[15] Steven P. Reise,et al. Item Response Theory , 2014 .
[16] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[17] Martin J. Wainwright,et al. Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$ -Constrained Quadratic Programming (Lasso) , 2009, IEEE Transactions on Information Theory.
[18] Joachim Giesen,et al. Ising Models with Latent Conditional Gaussian Variables , 2019, ALT.
[19] Gunter Maris,et al. Bayesian inference for low-rank Ising networks , 2015, Scientific Reports.
[20] G. Watson. Characterization of the subdifferential of some matrix norms , 1992 .
[21] Muni S. Srivastava,et al. Singular Wishart and multivariate beta distributions , 2003 .
[22] Onur Dikmen,et al. Consistent inference of a general model using the pseudolikelihood method. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.
[23] S Epskamp,et al. An Introduction to Network Psychometrics: Relating Ising Network Models to Item Response Theory Models , 2018, Multivariate behavioral research.