spcov: Sparse Estimation of a Covariance Matrix. Provides a covariance estimator for multivariate normal data that is sparse and positive definite. Implements the majorize-minimize algorithm described in Bien, J., and Tibshirani, R. (2011), ”Sparse Estimation of a Covariance Matrix,” Biometrika. 98(4). 807–820.
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References in zbMATH (referenced in 50 articles , 1 standard article )
Showing results 41 to 50 of 50.
- Ollier, Edouard; Samson, Adeline; Delavenne, Xavier; Viallon, Vivian: A SAEM algorithm for fused Lasso penalized nonlinear mixed effect models: application to group comparison in pharmacokinetics (2016)
- Parker, Ryan J.; Reich, Brian J.; Eidsvik, Jo: A fused Lasso approach to nonstationary spatial covariance estimation (2016)
- Shwartz, Ofer; Nadler, Boaz: Detecting the large entries of a sparse covariance matrix in sub-quadratic time (2016)
- Zhang, Lin; Sarkar, Abhra; Mallick, Bani K.: Bayesian sparse covariance decomposition with a graphical structure (2016)
- Chang, S.-M.: Double shrinkage estimators for large sparse covariance matrices (2015)
- Wang, Hao: Scaling it up: stochastic search structure learning in graphical models (2015)
- Rothman, Adam J.; Forzani, Liliana: On the existence of the weighted bridge penalized Gaussian likelihood precision matrix estimator (2014)
- Wang, Hao: Coordinate descent algorithm for covariance graphical Lasso (2014)
- Wang, Kaibo; Yeh, Arthur B.; Li, Bo: Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation (2014)
- Bien, Jacob; Tibshirani, Robert J.: Sparse estimation of a covariance matrix (2011)