Directions in Robust Statistics and Diagnositcs: Part I.- Survey of robust procedures for survival data.- Simulated annealing for the detection of multiple outliers using least squares and least median of squares fitting.- Goodness of fit tests and long-range dependence.- A functional approach to robust nonparametric regression.- Added variable plots in linear regression.- Efficiency of reweighted least squares iterates.- An overview of small sample asymptotics.- Diagnostics, divergences and perturbation analysis.- Some mixed questions and comments on robustness.- Some research directions in rank-based inference.- Between robustness and diagnostics.- Dependence among observations: Consequences and methods to deal with it.- Local and deletion influence.- Outliers in time series analysis: Some comments on their impact and their detection.- Breakdown point and asymptotic properties of multivariate S-estimators and r-estimators: A Summary.- Algorithms and programs for robust linear regression.- Robust M-type testing procedures for linear models.- Recent results on bias-robust regression estimates.- Bias robust estimation of autoregression parameters.- Author index.
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.
Springer Book Archives