I am looking to estimate a hierarchical GLM but with feature selection to determine which covariates are relevant at the population level to include.
Suppose I have $G$ groups with $N$ observations and $K$ possible covariates That is, I have design matrix of covariates $\boldsymbol{x}_{(N\cdot G) \times K}$, outcomes $\boldsymbol{y}_{(N\cdot G) \times 1}$. Coefficients on these covariates are $\beta_{K \times 1}$.
Suppose $Y$~$Bernoulli(p(x,\beta))$
The below is a standard hierarchical bayesian GLM with logit sampling model and normally distributed group coefficients.
$${\cal L}\left(\boldsymbol{y}|\boldsymbol{x},\beta_{1},...\beta_{G}\right)\propto\prod_{g=1}^{G}\prod_{t=1}^{N}\left(\Pr\{j=1|p_{t},\beta^{g}\}\right)^{y_{g,t}}\left(1-\Pr\{j=1|p_{t},\beta^{g}\}\right)^{1-y_{g,t}}$$
$$\beta_{1},...\beta_{G}|\mu,\Sigma\sim^{iid}{\cal N}_{d}\left(\mu,\Sigma\right)$$
$$\mu|\Sigma\sim{\cal N}\left(\mu_{0},a^{-1}\Sigma\right)$$ $$\Sigma\sim{\cal IW}\left(v_{0},V_{0}^{-1}\right)$$
I want to modify this model (or find a paper that does, or work that discusses it) in such a way that there is some sharp feature selection (as in LASSO) on the dimensionality of $\beta$.
(1) The simplest most direct way would be to regularize this at the population level so that we essentially restrict the dimensionality of $\mu$ and all $\beta$ have the same dimension.
(2) The more nuanced model would have shrinkage at the group level, where dimension of $\beta$ depends on the hierarhical unit.
I am interested in solving 1 and 2, but much more important is 1.