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Hero AO, Rajaratnam B. Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining. Proc IEEE Inst Electr Electron Eng. 2015;104(1):93-110doi: 10.1109/JPROC.2015.2494178.
Hero, A. O., & Rajaratnam, B. (2016). Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining. Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 104(1), 93-110. https://doi.org/10.1109/JPROC.2015.2494178
Hero, Alfred O, and Rajaratnam, Bala. "Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining." Proceedings of the IEEE. Institute of Electrical and Electronics Engineers vol. 104,1 (2016): 93-110. doi: https://doi.org/10.1109/JPROC.2015.2494178
Hero AO, Rajaratnam B. Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining. Proc IEEE Inst Electr Electron Eng. 2016 Jan;104(1):93-110. doi: 10.1109/JPROC.2015.2494178. Epub 2015 Dec 21. PMID: 27087700; PMCID: PMC4827453.
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Proc IEEE Inst Electr Electron Eng. 2016 Jan;104(1):93-110. doi: 10.1109/JPROC.2015.2494178. Epub 2015 Dec 21.

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining.

Proceedings of the IEEE. Institute of Electrical and Electronics Engineers

Alfred O Hero, Bala Rajaratnam

Affiliations

  1. University of Michigan, Ann Arbor, MI 48109-2122, USA.
  2. Stanford University, Stanford, CA 94305-4065, USA.

PMID: 27087700 PMCID: PMC4827453 DOI: 10.1109/JPROC.2015.2494178

Abstract

When can reliable inference be drawn in fue "Big Data" context? This paper presents a framework for answering this fundamental question in the context of correlation mining, wifu implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics fue dataset is often variable-rich but sample-starved: a regime where the number

Keywords: Big Data; asymptotic regimes; correlation estimation; correlation mining; correlation screening; correlation selection; graphical models; large scale inference; purely high dimensional; sample complexity; triple asymptotic framework; unifying learning theory

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