EAinference: Estimator Augmentation and Simulation-Based Inference

Estimator augmentation methods for statistical inference on high-dimensional data, as described in Zhou, Q. (2014) <arXiv:1401.4425v2> and Zhou, Q. and Min, S. (2017) <doi:10.1214/17-EJS1309>. 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.

Package details

AuthorSeunghyun Min [aut, cre], Qing Zhou [aut]
MaintainerSeunghyun Min <seunghyun@ucla.edu>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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EAinference documentation built on May 2, 2019, 3:36 p.m.