Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics)

Good, Phillip

ISBN 10: 1441919074 / ISBN 13: 9781441919076

Edité par Springer, Berlin, 2010

A propos de cet article

Paperback. Third edition. Several scores on covers. Spine foot and lower edge of rear cover are a little bumped, affecting closing pages. Light wear on spine head and leading corners. Front upper leading corner is slightly bent, affecting early pages. Minor film peeling on leading edge of rear cover. Binding is intact, contents are clean and clear. AM.

N° de réf. du vendeur 433379

Détails bibliographiques

• Titre: Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics)
• Éditeur: Springer, Berlin
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 1441919074
• ISBN à 13 chiffres: 9781441919076
• Reliure: Paperback
• État de l'article: Good
• Type de livre: Used
• Catalogues du vendeur:
  • Research Methods/Statistics/Information Sys

A propos de ce titre

Synopsis

This text is intended to provide a strong theoretical background in testing hypotheses and decision theory for those who will be practicing in the real worldorwhowillbeparticipatinginthetrainingofreal-worldstatisticiansand biostatisticians. In previous editions of this text, my rhetoric was somewhat tentative. I was saying, in e?ect, “Gee guys, permutation methods provide a practical real-world alternative to asymptotic parametric approximations. Why not give them a try?” But today, the theory, the software, and the hardware have come together. Distribution-free permutation procedures are the primary method for testing hypotheses. Parametric procedures and the bootstrap are to be reserved for the few situations in which they may be applicable. Four factors have forced this change: 1. Desire by workers in applied ?elds to use the most powerful statistic for their applications. Such workers may not be aware of the fundamental lemma of Neyman and Pearson, but they know that the statistic they wanttouse—acomplexscoreoraratioofscores,doesnothaveanalready well-tabulated distribution. 2. Pressure from regulatory agencies for the use of methods that yield exact signi?cance levels, not approximations. 3. A growing recognition that most real-world data are drawn from mixtures of populations. 4. A growing recognition that missing data is inevitable, balanced designs the exception. Thus, it seems natural that the theory of testing hypothesis and the more general decision theory in which it is embedded should be introduced via the permutation tests. On the other hand, certain relatively robust param- ric tests such as Student’s t continue to play an essential role in statistical practice.