Large-Scale Global and Simultaneous Inference: Estimation and Testing in Very High Dimensions
Tony Cai and Wenguang Sun
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Abstract:
Due to advances in technology and computing, researchers are now able to collect and analyze ever large data sets. In large-scale statistical inference, we often need to solve thousands and even millions of parallel problems simultaneously; this poses many challenges and calls for new techniques. The recent two decades have seen much excitement in the statistical community to address real current needs. A plethora of detection, estimation and testing techniques have been successfully developed and applied to a wide range of data-rich fields, including financial economics, marketing analytics, social science, signal processing, and biological sciences. This article reviews significant progresses that have been made in large-scale inference, with a focus on multiple testing and false discovery rate methodologies.
- Paper: pdf file.