Breaking the curse of dimensionality in regression

Understand

Models with many signals, high-dimensional models, often impose structures on the signal strengths.

• The common assumption is that only a few signals are strong and most of the signals are zero or close (collectively) to zero.
• However, such a requirement might not be valid in many real-life applications.
• In this article, we are interested in conducting large-scale inference in models that might have signals of mixed strengths.