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Confidence Intervals and Tests for High-Dimensional Models: A Compact Review

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Modeling and Stochastic Learning for Forecasting in High Dimensions

Part of the book series: Lecture Notes in Statistics ((LNSP,volume 217))

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Abstract

We present a compact review of methods for constructing tests and confidence intervals in high-dimensional models. Links to theory, finite sample performance results and software allows to obtain a “quick” but sufficiently deep overview for applying the procedures.

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Author information

Authors and Affiliations

  1. Seminar for Statistics, ETH Zürich, Zürich, Switzerland

    Peter Bühlmann

Authors
  1. Peter Bühlmann
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Correspondence to Peter Bühlmann .

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Editors and Affiliations

  1. Department of Statistics, University Joseph Fourier, Grenoble, France

    Anestis Antoniadis

  2. Laboratoire de Mathématiques, Université Paris-Sud, Orsay Cedex, France

    Jean-Michel Poggi

  3. Electricité de France R & D, OSIRIS, Clamart Cedex, France

    Xavier Brossat

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Bühlmann, P. (2015). Confidence Intervals and Tests for High-Dimensional Models: A Compact Review. In: Antoniadis, A., Poggi, JM., Brossat, X. (eds) Modeling and Stochastic Learning for Forecasting in High Dimensions. Lecture Notes in Statistics(), vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-18732-7_2

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