Breaking the curse of dimensionality in regression

  title={Breaking the curse of dimensionality in regression},
  author={Yinchu Zhu and Jelena Bradic},
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. The key challenge is that the signals that are not under testing… CONTINUE READING
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