adaHuber

Adaptive Huber Estimation and Regression

Description

This package implements the Huber-type estimator for mean, covariance matrix, regression and l₁-regularized Huber regression (Huber-Lasso). For all these methods, the robustification parameter τ is calibrated via a tuning-free principle.

Specifically, for Huber regression, assume the observed data vectors (Y, X) follow a linear model Y = θ₀ + X θ + ε, where Y is an n-dimensional response vector, X is an n × d design matrix, and ε is an n-vector of noise variables whose distributions can be asymmetric and/or heavy-tailed. The package computes the standard Huber’s M-estimator when d < n and the Huber-Lasso estimator when d > n. The vector of coefficients θ and the intercept term θ₀ are estimated successively via a two-step procedure. See Wang et al., 2021 for more details.

Recent update

2022-03-04

Version 1.1 is submitted to CRAN.

Installation

Install adaHuber from CRAN

install.packages("adaHuber")

Common error messages

• Error: Compilation failed (with messages involving lgfortran, clang, etc.). Solution: This is a compilation error of Rcpp-based source packages. It happens when we recently submit a new version to CRAN, but it usually takes 3-5 days to build the binary package. Please use an older version or patiently wait for 3-5 days and then install the updated version.

• Error: unable to load shared object.. Symbol not found: _EXTPTR_PTR. Solution: This issue is common in some specific versions of R when we load Rcpp-based libraries. It is an error in R caused by a minor change about EXTPTR_PTR. Upgrading R to 4.0.2 will solve the problem.

Functions

There are five functions in this package:

• adaHuber.mean: Adaptive Huber mean estimation.
• adaHuber.cov: Adaptive Huber covariance estimation.
• adaHuber.reg: Adaptive Huber regression.
• adaHuber.lasso: Adaptive Huber-Lasso regression.
• adaHuber.cv.lasso: Cross-validated adaptive Huber-Lasso regression.

Getting help

Help on the functions can be accessed by typing ?, followed by function name at the R command prompt.

For example, ?adaHuber.reg will present a detailed documentation with inputs, outputs and examples of the function adaHuber.reg.

Examples

First, we present an example of Huber mean estimation. We generate data from a t distribution, which is heavy-tailed. We estimate its mean by the tuning-free Huber mean estimator.

library(adaHuber)
n = 1000
mu = 2
X = rt(n, 2) + mu
fit.mean = adaHuber.mean(X)
fit.mean$mu

Then we present an example of Huber covariance matrix estimation. We generate data from t distribution with df = 3, which is heavy-tailed.

n = 100
p = 5
X = matrix(rt(n * p, 3), n, p)
fit.cov = adaHuber.cov(X)
fit.cov$cov

Next, we present an example of adaptive Huber regression. Here we generate data from a linear model Y = X θ + ε, where ε follows a t distribution, and estimate the intercept and coefficients by tuning-free Huber regression.

n = 200
p = 10
beta = rep(1.5, p + 1)
X = matrix(rnorm(n * p), n, p)
err = rt(n, 2)
Y = cbind(1, X) %*% beta + err

fit.adahuber = adaHuber.reg(X, Y, method = "adaptive")
beta.adahuber = fit.adahuber$coef

Finally, we illustrate the use of l₁-regularized Huber regression. Again, we generate data from a linear model Y = X θ + ε, where θ is a high-dimensional vector, and ε is from a t distribution. We estimate the intercept and coefficients by Huber-Lasso regression, where the regularization parameter λ is calibrated by K-fold cross-validation, and the robustification parameter τ is chosen by a tuning-free procedure.

n = 100; p = 200; s = 5
beta = c(rep(1.5, s + 1), rep(0, p - s))
X = matrix(rnorm(n * p), n, p)
err = rt(n, 2)
Y = cbind(rep(1, n), X) %*% beta + err 
 
fit.lasso = adaHuber.cv.lasso(X, Y)
beta.lasso = fit.lasso$coef

License

GPL-3.0

System requirements

C++11

Author(s)

Xiaoou Pan xip024@ucsd.edu, Wen-Xin Zhou wez243@ucsd.edu

Maintainer

Xiaoou Pan xip024@ucsd.edu

References

Eddelbuettel, D. and Francois, R. (2011). Rcpp: Seamless R and C++ integration. J. Stat. Softw. 40 1-18. Paper

Fan, J., Liu, H., Sun, Q. and Zhang, T. (2018). I-LAMM for sparse learning: Simultaneous control of algorithmic complexity and statistical error. Ann. Statist. 46 814–841. Paper

Ke, Y., Minsker, S., Ren, Z., Sun, Q. and Zhou, W.-X. (2019). User-friendly covariance estimation for heavy-tailed distributions. Statis. Sci. 34 454-471. Paper

Pan, X., Sun, Q. and Zhou, W.-X. (2021). Iteratively reweighted l1-penalized robust regression. Electron. J. Stat. 15 3287-3348. Paper

Sun, Q., Zhou, W.-X. and Fan, J. (2020). Adaptive Huber regression. J. Amer. Stat. Assoc. 115 254-265. Paper

Wang, L., Zheng, C., Zhou, W. and Zhou, W.-X. (2021). A new principle for tuning-free Huber regression. Stat. Sinica 31 2153-2177. Paper