Overview
- Authors:
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Peter Bloomfield
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Department of Statistics, North Carolina State Unversity, Raleigh, USA
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William L. Steiger
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Department of Computer Science, Rutgers University, New Brunswick, USA
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Table of contents (8 chapters)
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- Peter Bloomfield, William L. Steiger
Pages 1-36
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- Peter Bloomfield, William L. Steiger
Pages 37-76
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- Peter Bloomfield, William L. Steiger
Pages 77-108
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- Peter Bloomfield, William L. Steiger
Pages 109-130
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- Peter Bloomfield, William L. Steiger
Pages 131-151
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- Peter Bloomfield, William L. Steiger
Pages 152-180
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- Peter Bloomfield, William L. Steiger
Pages 181-274
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- Peter Bloomfield, William L. Steiger
Pages 276-325
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Back Matter
Pages 326-351
About this book
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.
Authors and Affiliations
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Department of Statistics, North Carolina State Unversity, Raleigh, USA
Peter Bloomfield
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Department of Computer Science, Rutgers University, New Brunswick, USA
William L. Steiger