Quantile regression via iterative least squares computations

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Abstract

We present an estimating framework for quantile regression where the usual L1-norm objective function is replaced by its smooth parametric approximation. An exact path-following algorithm is derived, leadingto the well-known ‘basic’ solutions interpolating exactly a number of observations equal to the number ofparameters being estimated. We discuss briefly possible practical implications of the proposed approach, such as early stopping for large data sets, confidence intervals, and additional topics for future research.
Original languageEnglish
Pages (from-to)1557-1569
Number of pages0
JournalJournal of Statistical Computation and Simulation
Volume82
Publication statusPublished - 2012

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All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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