Bayesian linear regression for multivariate responses under group sparsity

Bayesian regression with multivariate linear splines

Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure.

We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associat...

Boosting for High-multivariate Responses in High-dimensional Linear Regression

We propose a boosting method, multivariate L2Boosting, for multivariate linear regression based on some squared error loss for multivariate data. It can be applied to multivariate linear regression with continuous responses and to vector autoregressive time series. We prove, for i.i.d. as well as time series data, that multivariate L2Boosting can consistently recover sparse high-dimensional mul...

Bayesian multivariate logistic regression.

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic dis...

Bayesian Robust Multivariate Linear Regression With Incomplete Data

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