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Confidence Intervals Using the Lasso

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Part of the Lecture Notes in Mathematics book series (LNMECOLE,volume 2159)

Abstract

Asymptotic linearity of a de-sparsified Lasso is established. This implies asymptotic normality under certain conditions and therefore can be used to construct confidence intervals for parameters of interest. Asymptotic linearity of groups of parameters, leading to confidence sets for groups, is also presented. Here, a the multivariate version of the square-root Lasso is invoked. The case of a linearized loss—applicable when the covariance matrix of the design is known—is briefly addressed as well. Throughout the chapter except for the last section, the design is considered as fixed.

Keywords

  • Tuning Parameter
  • Asymptotic Normality
  • Remainder Term
  • Linear Contrast
  • Surrogate Projection

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.

    The number of parameters is pq in the matrix completion problem of Sect. 12.5.

References

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© 2016 Springer International Publishing Switzerland

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van de Geer, S. (2016). Confidence Intervals Using the Lasso. In: Estimation and Testing Under Sparsity. Lecture Notes in Mathematics(), vol 2159. Springer, Cham. https://doi.org/10.1007/978-3-319-32774-7_5

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