Least squares and least absolute deviation procedures in approximately linear models

Thomas Mathew, Kenneth Nordström

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

The Approximately Linear Model, introduced by Sacks and Ylvisaker (1978, The Annals of Statistics), represents deviations from the ideal linear model y = Xβ + e, by considering y = b + Xβ + e, where b is an unknown bias vector whose components are bounded in absolute value, i.e., |bi| ≤ ri, ri being a known nonnegative number. We propose to estimate β by minimizing the maximum of a weighted sum of squared deviations, or the sum of absolute deviations, where the maximum is computed subject to |bi| ≤ ri. In the former case the criterion to be minimized turns out to be a linear combination of the least squares and least absolute deviation criteria for the ideal linear model. The estimate of β obtained by the latter approach (i.e., by minimizing the maximum of a weighted sum of absolute deviations) turns out to be independent of the assumed bound ri on bi. This establishes another robustness property of the least absolute deviation criterion.

Original languageEnglish
Pages (from-to)153-158
Number of pages6
JournalStatistics and Probability Letters
Volume16
Issue number2
DOIs
Publication statusPublished - Jan 27 1993

Keywords

  • Approximately linear model
  • least absolute deviation
  • least squares

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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