Nonparametric Goodness-of-Fit Testing Under Gaussian Models / Lecture Notes in Statistics Bd.169 (PDF)
This book presents the modern theory of nonparametric goodness-of-fit testing. It fills the gap in modern nonparametric statistical theory by discussing hypothesis testing and addresses mathematical statisticians who are interesting in the theory of...
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This book presents the modern theory of nonparametric goodness-of-fit testing. It fills the gap in modern nonparametric statistical theory by discussing hypothesis testing and addresses mathematical statisticians who are interesting in the theory of non-parametric statistical inference. It will be of interest to specialists who are dealing with applied non-parametric statistical problems relevant in signal detection and transmission and in technical and medical diagnostics among others.
- Autoren: Yuri Ingster , I. A. Suslina
- 2012, 2003, 457 Seiten, Englisch
- Verlag: Springer US
- ISBN-10: 0387215808
- ISBN-13: 9780387215808
- Erscheinungsdatum: 12.11.2012
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 31 MB
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