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Directions in Robust Statistics and Diagnostics

Part II

  • Werner Stahel
  • Sanford Weisberg

Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 34)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Joseph W. Mckean, Simon J. Sheather, Thomas P. Hettmansperger
    Pages 21-31
  3. Luciano Molinari, Guido Dumermuth
    Pages 33-47
  4. Stephan Morgenthaler
    Pages 49-63
  5. Neyko M. Neykov, Plamen N. Neytchev
    Pages 115-128
  6. Carl M. O’Brien
    Pages 129-143
  7. Helmut Rieder
    Pages 159-183
  8. Peter J. Rousseeuw, Gilbert W. Bassett Jr.
    Pages 185-194
  9. Peter J. Rousseeuw, Bert C. van Zomeren
    Pages 195-203
  10. Robert Schall, Timothy T. Dunne
    Pages 205-221
  11. Werner A. Stahel
    Pages 243-278
  12. John W. Tukey
    Pages 297-308
  13. Halbert White, Maxwell Stinchcombe
    Pages 337-363
  14. Victor J. Yohai, Werner A. Stahel, Ruben H. Zamar
    Pages 365-374
  15. Back Matter
    Pages 375-380

About these proceedings

Introduction

This IMA Volume in Mathematics and its Applications DIRECTIONS IN ROBUST STATISTICS AND DIAGNOSTICS is based on the proceedings of the first four weeks of the six week IMA 1989 summer program "Robustness, Diagnostics, Computing and Graphics in Statistics". An important objective of the organizers was to draw a broad set of statisticians working in robustness or diagnostics into collaboration on the challenging problems in these areas, particularly on the interface between them. We thank the organizers of the robustness and diagnostics program Noel Cressie, Thomas P. Hettmansperger, Peter J. Huber, R. Douglas Martin, and especially Werner Stahel and Sanford Weisberg who edited the proceedings. A vner Friedman Willard Miller, Jr. PREFACE Central themes of all statistics are estimation, prediction, and making decisions under uncertainty. A standard approach to these goals is through parametric mod­ elling. Parametric models can give a problem sufficient structure to allow standard, well understood paradigms to be applied to make the required inferences. If, how­ ever, the parametric model is not completely correct, then the standard inferential methods may not give reasonable answers. In the last quarter century, particularly with the advent of readily available computing, more attention has been paid to the problem of inference when the parametric model used is not correctly specified.

Keywords

algorithms data analysis linear regression robust statistics statistics time series

Authors and affiliations

  • Werner Stahel
    • 1
  • Sanford Weisberg
    • 2
  1. 1.Seminar für StatistikSwiss Federal Institute of TechnologyZürichSwitzerland
  2. 2.Department of MathematicsUniversity of MinnesotaSt. PaulUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-4444-8
  • Copyright Information Springer-Verlag New York 1991
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8772-8
  • Online ISBN 978-1-4612-4444-8
  • Series Print ISSN 0940-6573
  • Buy this book on publisher's site