Estimator Augmentation and Simulation-Based Inference
Compute K-fold cross-validated mean squared error for lasso
Compute importance weights for lasso, group lasso, scaled lasso or sca...
Compute lasso estimator
Metropolis-Hastings lasso sampler under a fixed active set.
Provide (1-alpha)% confidence interval of each coefficients
Parametric bootstrap sampler for lasso, group lasso, scaled lasso or s...
Plot Metropolis-Hastings sampler outputs
Post-inference with lasso estimator
Print Metropolis-Hastings sampler outputs
Summarizing Metropolis-Hastings sampler outputs
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.