Directions in Robust Statistics and Diagnostics: Part II. The IMA Volumes in Mathematics and its Applications Volume 34

Stahel, Werner and Sanford Weisberg eds

ISBN 10: 0387975314 / ISBN 13: 9780387975313

Published by New York: Springer, 1991

About this Item

1991. Yellow and red cloth hardcover. Glossy-finish hardcover. No jacket issued. 380pp. FINE.

Seller Inventory # 2652614

Bibliographic Details

• Title: Directions in Robust Statistics and Diagnostics: Part II. The IMA Volumes in Mathematics and its Applications Volume 34
• Publisher: New York: Springer
• Publication Date: 1991
• ISBN 10: 0387975314
• ISBN 13: 9780387975313
• Binding: Hardcover
• Condition: FINE
• Seller catalogs:
  • General non-fiction

About this title

Synopsis

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.