conquer - Convolution-Type Smoothed Quantile Regression

Estimation and inference for conditional linear quantile regression models using a convolution smoothed approach. In the low-dimensional setting, efficient gradient-based methods are employed for fitting both a single model and a regression process over a quantile range. Normal-based and (multiplier) bootstrap confidence intervals for all slope coefficients are constructed. In high dimensions, the conquer method is complemented with flexible types of penalties (Lasso, elastic-net, group lasso, sparse group lasso, scad and mcp) to deal with complex low-dimensional structures.

Last updated 2 years ago

openblascppopenmp

6.09 score 19 stars 5 packages 17 scripts 5.0k downloads

adaHuber - Adaptive Huber Estimation and Regression

Huber-type estimation for mean, covariance and (regularized) regression. For all the methods, the robustification parameter tau is chosen by a tuning-free principle.

Last updated 3 years ago

3.78 score 12 stars 6 scripts 361 downloads

FarmTest - Factor-Adjusted Robust Multiple Testing

Performs robust multiple testing for means in the presence of known and unknown latent factors presented in Fan et al.(2019) "FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control" <doi:10.1080/01621459.2018.1527700>. Implements a series of adaptive Huber methods combined with fast data-drive tuning schemes proposed in Ke et al.(2019) "User-Friendly Covariance Estimation for Heavy-Tailed Distributions" <doi:10.1214/19-STS711> to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions. Extensions to two-sample simultaneous mean comparison problems are also included. As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest.

Last updated 4 years ago

3.48 score 4 stars 15 scripts 221 downloads