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Least Absolute Deviations

Theory, Applications and Algorithms

  • Peter Bloomfield
  • William L. Steiger

Part of the Progress in Probability and Statistics book series (PRPR, volume 6)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Peter Bloomfield, William L. Steiger
    Pages 1-36
  3. Peter Bloomfield, William L. Steiger
    Pages 37-76
  4. Peter Bloomfield, William L. Steiger
    Pages 77-108
  5. Peter Bloomfield, William L. Steiger
    Pages 109-130
  6. Peter Bloomfield, William L. Steiger
    Pages 131-151
  7. Peter Bloomfield, William L. Steiger
    Pages 152-180
  8. Peter Bloomfield, William L. Steiger
    Pages 181-274
  9. Peter Bloomfield, William L. Steiger
    Pages 276-325
  10. Back Matter
    Pages 326-351

About this book

Introduction

Least squares is probably the best known method for fitting linear models and by far the most widely used. Surprisingly, the discrete L 1 analogue, least absolute deviations (LAD) seems to have been considered first. Possibly the LAD criterion was forced into the background because of the com­ putational difficulties associated with it. Recently there has been a resurgence of interest in LAD. It was spurred on by work that has resulted in efficient al­ gorithms for obtaining LAD fits. Another stimulus came from robust statistics. LAD estimates resist undue effects from a feyv, large errors. Therefore. in addition to being robust, they also make good starting points for other iterative, robust procedures. The LAD criterion has great utility. LAD fits are optimal for linear regressions where the errors are double exponential. However they also have excellent properties well outside this narrow context. In addition they are useful in other linear situations such as time series and multivariate data analysis. Finally, LAD fitting embodies a set of ideas that is important in linear optimization theory and numerical analysis. viii PREFACE In this monograph we will present a unified treatment of the role of LAD techniques in several domains. Some of the material has appeared in recent journal papers and some of it is new. This presentation is organized in the following way. There are three parts, one for Theory, one for Applicatior.s and one for Algorithms.

Keywords

algorithms linear optimization numerical analysis optimization statistics

Authors and affiliations

  • Peter Bloomfield
    • 1
  • William L. Steiger
    • 2
  1. 1.Department of StatisticsNorth Carolina State UnversityRaleighUSA
  2. 2.Department of Computer ScienceRutgers UniversityNew BrunswickUSA

Bibliographic information