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Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm
We use empirical Bayes estimators of hyperparameters for increased flexibility and an Expectation-Conditional-Minimization (ECM) algorithm for computationally efficient maximum a posteriori probability (MAP) estimation of parameters.
Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression
Bayesian heteroscedastic linear regression models relax the homoscedastic error assumption but can enforce restrictive prior assumptions on parameters, and many are computationally infeasible in the high-dimensional setting.
Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm
In this paper, we proposed a computationally efficient and powerful Bayesian approach for sparse high-dimensional linear regression.
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