covreg: A simultaneous regression model for the mean and covariance. This package fits a simultaneous regression model for the mean vectors and covariance matrices of multivariate response variables, as described in Hoff and Niu (2012). The explanatory variables can be continuous or discrete. The current version of the package provides the Bayesian estimates.
Keywords for this software
References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Sarkar, Abhra; Pati, Debdeep; Chakraborty, Antik; Mallick, Bani K.; Carroll, Raymond J.: Bayesian semiparametric multivariate density deconvolution (2018)
- Lee, Keunbaik; Baek, Changryong; Daniels, Michael J.: ARMA Cholesky factor models for the covariance matrix of linear models (2017)
- Mark O’Connell, Catherine B. Hurley, Katarina Domijan: Conditional Visualization for Statistical Models: An Introduction to the condvis Package in R (2016) arXiv
- Zhang, Lin; Sarkar, Abhra; Mallick, Bani K.: Bayesian sparse covariance decomposition with a graphical structure (2016)
- Fox, Emily B.; Dunson, David B.: Bayesian nonparametric covariance regression (2015)
- Valcarcel Salamanca, Beatriz; Ebbels, Timothy M. D.; De Iorio, Maria: Variance and covariance heterogeneity analysis for detection of metabolites associated with cadmium exposure (2014)
- Wang, Y.; Daniels, M. J.: Bayesian modeling of the dependence in longitudinal data via partial autocorrelations and marginal variances (2013)
- Hoff, Peter D.; Niu, Xiaoyue: A covariance regression model (2012)
- Pourahmadi, Mohsen: Covariance estimation: the GLM and regularization perspectives (2011)