Estimator Augmentation and Simulation-Based Inference
Description
Estimator augmentation methods for statistical inference on high-dimensional data,
as described in Zhou, Q. (2014)
and Zhou, Q. and Min, S. (2017) .
It provides several simulation-based inference methods: (a) Gaussian and
wild multiplier bootstrap for lasso, group lasso, scaled lasso, scaled group
lasso and their de-biased estimators, (b) importance sampler for approximating
p-values in these methods, (c) Markov chain Monte Carlo lasso sampler with
applications in post-selection inference.