Notice bibliographique
- Notice
Type(s) de contenu et mode(s) de consultation : Texte noté : électronique
Auteur(s) : Good, Phillip I.
Titre(s) : Permutation, parametric and bootstrap tests of hypotheses [Texte électronique] / Phillip Good
Titre d'ensemble : Springer e-books
Édition : 3rd ed.
Publication : New York : Springer, cop. 2005
Description matérielle : 1 online resource (xix, 315 pages)
Collection : Springer series in statistics
Note(s) : Includes bibliographical references (pages 279-302)-and indexes
This text will equip both practitioners and theorists with the necessary background
in testing hypothesis and decision theory to enable innumerable practical applications
of statistics. Its intuitive and informal style makes it suitable as a text for both
students and researchers. It can serve as the basis a one- or two-semester graduate
course as well as a standard handbook of statistical procedures for the practitioners{u2019}
desk. Parametric, permutation, and bootstrap procedures for testing hypotheses are
developed side by side. The emphasis on distribution-free permutation procedures will
enable workers in applied fields to use the most powerful statistic for their applications
and satisfy regulatory agency demands for methods that yield exact significance levels,
not approximations. Algebra and an understanding of discrete probability will take
the reader through all but the appendix, which utilizes probability measures in its
proofs. The revised and expanded text of the 3rd edition includes many more real-world
illustrations from biology, business, clinical trials, economics, geology, law, medicine,
social science and engineering along with twice the number of exercises. Real-world
problems of missing and censored data, multiple comparisons, nonresponders, after-the-fact
covariates, and outliers are dealt with at length. New sections are added on sequential
analysis and multivariate analysis plus a chapter on the exact analysis of multi-factor
designs based on the recently developed theory of synchronous permutations. The book's
main features include: Detailed consideration of one-, two-, and k-sample tests, contingency
tables, clinical trials, cluster analysis, multiple comparisons, multivariate analysis,
and repeated measures Numerous practical applications in archeology, biology, business,
climatology, clinical trials, economics, education, engineering, geology, law, medicine,
and the social sciences Valuable techniques for reducing computation time Practical
advice on experimental design Sections on sequential analysis Comparisons among competing
bootstrap, parametric, and permutation techniques. From a review of the first edition:
"Permutation Tests is a welcome addition to the literature on this subject and will
prove a valuable guide for practitioners ... This book has already become an important
addition to my reference library. Those interested in permutation tests and its applications
will enjoy reading it." (Journal of the American Statistical Association) From a review
of the second edition: "Permutation Tests is superb as a resource for practitioners.
The text covers a broad range of topics, and has myriad pointers to topics not directly
addressed. . . the book gives guidance and inspiration to encourage developing one{u2019}s
own perfectly tailored statistics{u2026}The writing is fun to read." (John I. Marden)
Sujet(s) : Analyse multivariée -- Statistique
Bootstrap (statistique)
Statistique mathématique -- Théorie asymptotique
Tests d'hypothèses (statistique)
Rééchantillonnage (statistique)
Indice(s) Dewey :
519.56 (23e éd.) = Vérification des hypothèses (statistique mathématique)
Identifiants, prix et caractéristiques : ISBN 9780387271583
Identifiant de la notice : ark:/12148/cb446417331
Notice n° :
FRBNF44641733
(notice reprise d'un réservoir extérieur)
Table des matières : A Wide Range of Applications; Optimal Procedures; Testing Hypotheses; Distributions;
Multiple Tests; Experimental Designs; Multifactor Designs; Categorical Data; Multivariate
Analysis; Clustering in Time and Space; Coping with Disaster; Solving the Unsolved
and the Insolvable; Publishing Your Results; Increasing Computational Efficiency.