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An asymptotically minimax kernel machine
Statistics and Probability Letters (2014)
  • Debashis Ghosh, university of colorado denver
Abstract

Recently, a class of machine learning-inspired procedures, termed kernel machine methods, has been extensively developed in the statistical literature. It has been shown to have large power for a wide class of problems and applications in genomics and brain imaging. Many authors have exploited an equivalence between kernel machines and mixed e ects models and used attendant estimation and inferential procedures. In this note, we construct a so-called `adaptively minimax' kernel machine. Such a construction highlights the role of thresholding in the observation space and limits on the interpretability of such kernel machines.

Publication Date
2014
Citation Information
Debashis Ghosh. "An asymptotically minimax kernel machine" Statistics and Probability Letters (2014)
Available at: http://works.bepress.com/debashis_ghosh/63/