Adaptive Posterior Mode Estimation of a Sparse Sequence for Model Selection
نویسنده
چکیده
For the problem of estimating a sparse sequence of coefficients of a parametric or nonparametric generalized linear model, posterior mode estimation with a Subbotin(λ, ν) prior achieves thresholding and therefore model selection when ν ∈ [0, 1] for a class of likelihood functions. The proposed estimator also offers a continuum between the (forward/backward) best subset estimator (ν = 0), its approximate convexification called lasso (ν = 1) and ridge regression (ν = 2).
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