2014

Robust Estimation of High-Dimensional Mean Regression

Fan, Jianqing, Li, Quefeng, Wang, Yuyan

Understand

Data subject to heavy-tailed errors are commonly encountered in various scientific fields, especially in the modern era with explosion of massive data.

To address this problem, procedures based on quantile regression and Least Absolute Deviation (LAD) regression have been devel- oped in recent years.
These methods essentially estimate the conditional median (or quantile) function.
They can be very different from the conditional mean functions when distributions are asymmetric and heteroscedastic.

Built on

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Similar

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