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

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.

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Debashis Ghosh. "An asymptotically minimax kernel machine" Statistics and Probability Letters (2014)
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