Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions

Data subject to heavy-tailed errors are commonly encountered in various scientific fields. To address this problem, procedures based on quantile regression and least absolute deviation regression have been developed in recent years. These methods essentially estimate the conditional median (or quant...

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Bibliographic Details
Published in:Journal of the Royal Statistical Society. Series B, Statistical methodology Vol. 79; no. 1; pp. 247 - 265
Main Authors: Fan, Jianqing, Li, Quefeng, Wang, Yuyan
Format: Journal Article
Language:English
Published: England John Wiley & Sons Ltd 01.01.2017
Oxford University Press
Subjects:
algorithms
Analysis
Byproducts
Convergence
Descent
Deviation
equations
Errors
Estimators
heteroskedasticity
High dimension
Huber loss
Inequality
Mathematical analysis
Mathematical models
M‐estimator
Optimal rate
Quantiles
Quantitative analysis
Regression
Regression analysis
Robust regularization
Simulation
Statistical methods
Statistics
Studies
Symmetry
High dimension
Robust regularization
Huber loss
M-estimator
Optimal rate
ISSN:1369-7412, 1467-9868
Online Access:Get full text
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Summary:Data subject to heavy-tailed errors are commonly encountered in various scientific fields. To address this problem, procedures based on quantile regression and least absolute deviation regression have been developed in recent years. These methods essentially estimate the conditional median (or quantile) function. They can be very different from the conditional mean functions, especially when distributions are asymmetric and heteroscedastic. How can we efficiently estimate the mean regression functions in ultrahigh dimensional settings with existence of only the second moment? To solve this problem, we propose a penalized Huber loss with diverging parameter to reduce biases created by the traditional Huber loss. Such a penalized robust approximate (RA) quadratic loss will be called the RA lasso. In the ultrahigh dimensional setting, where the dimensionality can grow exponentially with the sample size, our results reveal that the RA lasso estimator produces a consistent estimator at the same rate as the optimal rate under the light tail situation. We further study the computational convergence of the RA lasso and show that the composite gradient descent algorithm indeed produces a solution that admits the same optimal rate after sufficient iterations. As a by-product, we also establish the concentration inequality for estimating the population mean when there is only the second moment. We compare the RA lasso with other regularized robust estimators based on quantile regression and least absolute deviation regression. Extensive simulation studies demonstrate the satisfactory finite sample performance of the RA lasso.
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ISSN:1369-7412
1467-9868
DOI:10.1111/rssb.12166