Nonparametric Tests for Complete Data (PDF)
(Sprache: Englisch)
This book concerns testing hypotheses in non-parametric models.
Classical non-parametric tests (goodness-of-fit, homogeneity,
randomness, independence) of complete data are considered. Most of
the test results are proved and real applications are...
Classical non-parametric tests (goodness-of-fit, homogeneity,
randomness, independence) of complete data are considered. Most of
the test results are proved and real applications are...
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This book concerns testing hypotheses in non-parametric models.
Classical non-parametric tests (goodness-of-fit, homogeneity,
randomness, independence) of complete data are considered. Most of
the test results are proved and real applications are illustrated
using examples. Theories and exercises are provided. The incorrect
use of many tests applying most statistical software is highlighted
and discussed.
Classical non-parametric tests (goodness-of-fit, homogeneity,
randomness, independence) of complete data are considered. Most of
the test results are proved and real applications are illustrated
using examples. Theories and exercises are provided. The incorrect
use of many tests applying most statistical software is highlighted
and discussed.
Autoren-Porträt von Vilijandas Bagdonavièus, Julius Kruopis, Mikhail Nikulin
Vilijandas Bagdonavicius is Professor of Mathematics at the University of Vilnius in Lithuania. His main research areas are statistics, reliability and survival analysis.Julius Kruopis is Associate Professor of Mathematics at the University of Vilnius in Lithuania. His main research areas are statistics and quality control.
Mikhail S. Nikulin is a member of the Institute of Mathematics in Bordeaux, France.
Bibliographische Angaben
- Autoren: Vilijandas Bagdonavièus , Julius Kruopis , Mikhail Nikulin
- 2013, 1. Auflage, 320 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 1118601602
- ISBN-13: 9781118601600
- Erscheinungsdatum: 04.02.2013
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