Directions in Robust Statistics and Diagnostics

1. Front Matter

   Pages i-xviii

   PDF

2. Small Sample Properties of Robust Analyses of Linear Models Based on R-Estimates: A Survey

   • Joseph W. McKean, Simon J. Sheather

   Pages 1-19

3. Regression Diagnostics for Rank-Based Methods II

   • Joseph W. Mckean, Simon J. Sheather, Thomas P. Hettmansperger

   Pages 21-31

4. Robust Multivariate Spectral Analysis of the EEG

   • Luciano Molinari, Guido Dumermuth

   Pages 33-47

5. Configural Polysampling

   • Stephan Morgenthaler

   Pages 49-63

6. Robustness to Unequal Scale and Other Departures from the Classical Linear Model

   • Nuwan Nanayakkara, Noel Cressie

   Pages 65-113

7. Unmasking Multivariate Outliers and Leverage Points by Means of BMDP3R

   • Neyko M. Neykov, Plamen N. Neytchev

   Pages 115-128

8. Glimpse: An Assessor of GLM Misspecification

   • Carl M. O’Brien

   Pages 129-143

9. Regression Quantile Diagnostics for Multiple Outliers

   • Stephen Portnoy

   Pages 145-157

10. Robust Testing of Functionals

    • Helmut Rieder

    Pages 159-183

11. Robustness of the p-Subset Algorithm for Regression with High Breakdown Point

    • Peter J. Rousseeuw, Gilbert W. Bassett Jr.

    Pages 185-194

12. Robust Distances: Simulations and Cutoff Values

    • Peter J. Rousseeuw, Bert C. van Zomeren

    Pages 195-203

13. Diagnostics for Regression-Arma Time Series

    • Robert Schall, Timothy T. Dunne

    Pages 205-221

14. General Approaches to Stepwise Identification of Unusual Values in Data Analysis

    • Jeffrey S. Simonoff

    Pages 223-242

15. Research Directions in Robust Statistics

    • Werner A. Stahel

    Pages 243-278

16. Comparisons Between First Order and Second Order Approximations in Regression Diagnostics

    • Chih-Ling Tsai, Xizhi Wu

    Pages 279-295

17. Consumer Datesware

    • John W. Tukey

    Pages 297-308

18. Graphical Displays for Alternate Regression Fits

    • John W. Tukey

    Pages 309-325

19. Some Issues in the Robust Estimation of Multivariate Location and Scatter

    • David E. Tyler

    Pages 327-336

20. Adaptive Efficient Weighted Least Squares With Dependent Observations

    • Halbert White, Maxwell Stinchcombe

    Pages 337-363

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.

• algorithms
• data analysis
• linear regression
• robust statistics
• statistics
• time series

• Seminar für Statistik, Swiss Federal Institute of Technology, Zürich, Switzerland

  Werner Stahel

• Department of Mathematics, University of Minnesota, St. Paul, USA

  Sanford Weisberg

Policies and ethics