Theory and Applications of Recent Robust MethodsMia Hubert Springer Science & Business Media, 2004 M06 25 - 400 pages The International Conference for Robust Statistics 2003, ICORS 2003, took place at the University of Antwerp, Belgium, from July 13-18. The conference was intended to be a forum where all aspects of robust statistics could be discussed. As such the scientific program included a wide range of talks on new developments and practice of robust statistics, with applications to finance, chemistry, engineering, and other fields. Of equal interest were interactions between robustness and other fields of statistics, and science in general. This volume offers a wide range of papers that were presented at the conference. Several articles primarily contain new methods and theoretical results, while others investigate empirical properties, discuss computational aspects, or emphasize applications of robust methods. Many contributions contain links to other fields, such as computer vision, computational geometry, chemometrics and finance. Intended for both researchers and practitioners, this book will be a valuable resource for studying and applying recent robust statistical methods. |
From inside the book
Results 1-5 of 84
Page vi
... Analysis J. Jurečková and J. Picek 119 131 Estimates of the Tail Index Based on Nonparametric Tests 141 A. Kankainen , S. Taskinen and H. Oja On Mardia's Tests of Multinormality 153 A. Kharin Robustness in Sequential Discrimination of ...
... Analysis J. Jurečková and J. Picek 119 131 Estimates of the Tail Index Based on Nonparametric Tests 141 A. Kankainen , S. Taskinen and H. Oja On Mardia's Tests of Multinormality 153 A. Kharin Robustness in Sequential Discrimination of ...
Page 13
Sorry, this page's content is restricted.
Sorry, this page's content is restricted.
Page 14
Sorry, this page's content is restricted.
Sorry, this page's content is restricted.
Page 23
Sorry, this page's content is restricted.
Sorry, this page's content is restricted.
Page 24
Sorry, this page's content is restricted.
Sorry, this page's content is restricted.
Contents
III | 1 |
IV | 13 |
V | 27 |
VI | 39 |
VII | 49 |
VIII | 59 |
IX | 71 |
X | 83 |
XXI | 207 |
XXII | 221 |
XXIII | 235 |
XXIV | 247 |
XXV | 259 |
XXVI | 271 |
XXVII | 283 |
XXVIII | 297 |
XI | 93 |
XII | 105 |
XIII | 119 |
XIV | 131 |
XV | 141 |
XVI | 153 |
XVII | 165 |
XVIII | 173 |
XIX | 183 |
XX | 195 |
XXX | 307 |
XXXI | 319 |
XXXII | 329 |
XXXIII | 343 |
XXXIV | 355 |
XXXV | 367 |
XXXVI | 377 |
XXXVII | 387 |
Other editions - View all
Theory and Applications of Recent Robust Methods Mia Hubert,Greet Pison,Anja Struyf,Stefan Van Aelst Limited preview - 2012 |
Theory and Applications of Recent Robust Methods Mia Hubert,Greet Pison,Anja Struyf No preview available - 2004 |
Common terms and phrases
2004 Birkhäuser Verlag affine equivariant algorithm Amer aß-Trimmed asymptotic Birkhäuser Verlag Basel/Switzerland bivariate bounded classical coefficients components computer vision conditional consider contamination covariance matrix Croux data points data set defined denote density depth e-mail efficiency EMM estimator empirical equivariant ERTBS Figure Gaussian Hubert inflation influence function kernel L-estimator Least Median Lemma linear location estimator Mathematics Subject Classification maximum likelihood Mean Square Errors Median of Squares methods minimization multivariate normal distribution Neykov normal distribution observations obtained outliers P.J. Rousseeuw parameters Pareto plot problem procedure proposed quadratic quantile redundancy regression model residuals rMCD robust estimators robust measures Robust Regression Robust Statistics rOGK RPCR S₁ sample scale estimators Section simulation smoothed solution standard Subject Classification 2000 support vector machine tail technique Theorem tion transformation Type I error uvec Van Driessen variance weight Yohai