Robust Statistics
(Sprache: Englisch)
The traditional setting of statistical inference involves finite or known spaces and subjects. During the past few decades, a theory has been developed that allows the sample space to be abstract or unknown. More recently, mathematical techniques-especially...
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Klappentext zu „Robust Statistics “
The traditional setting of statistical inference involves finite or known spaces and subjects. During the past few decades, a theory has been developed that allows the sample space to be abstract or unknown. More recently, mathematical techniques-especially the method of sieves-have been constructed to enable inferences to be made in abstract parameter spaces. This work began with the author's 1950 monograph on inferencein stochastic processes (for general sample space) and with the sieve methodology (for general parameter space) This paperback reprint of a Wiley bestseller studies both of these cases and represents the first comprehensive treatment of the subject.
Inhaltsverzeichnis zu „Robust Statistics “
1. Generalities.2. The Weak Topology and Its Metrization.3. The Basic Types of Estimates.4. Asymptotic Minimax Theory for Estimating a Location Parameter.5. Scale Estimates.6. Multiparameter Problems, In Particular Joint Estimation of Location and Scale.7. Regression.8. Robust Covariance and Correlation Matrices.9. Rubustness of Design.10. Exact Finite Sample Results.11. Miscellaneous Topics.References.Index.
Bibliographische Angaben
- Autor: Peter J. Huber
- 2004, 308 Seiten, Maße: 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Wiley & Sons
- ISBN-10: 0471650722
- ISBN-13: 9780471650720
Sprache:
Englisch
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