Large Scale Signal Detection: A Unified Perspective

TL;DR

This paper unifies various large-scale inference methods, clarifies their connections, offers simpler derivations, and develops a practical algorithm, demonstrated through applications to real and simulated data.

Contribution

It provides a unified framework connecting existing methods, simplifies their formulas, and offers a practical algorithm for large-scale signal detection.

Findings

Demonstrated the unified approach on real datasets

Provided simpler derivations of existing formulas

Developed a practical algorithm for practitioners

Abstract

There is an overwhelmingly large literature and algorithms already available on `large scale inference problems' based on different modeling techniques and cultures. Our primary goal in this paper is \emph{not to add one more new methodology} to the existing toolbox but instead (a) to clarify the mystery how these different simultaneous inference methods are \emph{connected}, (b) to provide an alternative more intuitive derivation of the formulas that leads to \emph{simpler} expressions, and (c) to develop a \emph{unified} algorithm for practitioners. A detailed discussion on representation, estimation, inference, and model selection is given. Applications to a variety of real and simulated datasets show promise. We end with several future research directions.