Trend filtering uses the generalized lasso framework to fit an adaptive polynomial of degree k to estimate the function f_0 at each input x_i in the model: y_i = f_0(x_i) + epsilon_i, for i = 1, ..., n, and epsilon_i is sub-Gaussian with E(epsilon_i) = 0. Bayesian trend filtering adapts the genlasso framework to a fully Bayesian hierarchical model, estimating the penalty parameter lambda within a tractable Gibbs sampler.
|Author||Edward A. Roualdes|
|Maintainer||Edward A. Roualdes <[email protected]>|
|License||GPL (>= 2.0)|
|Package repository||View on CRAN|
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