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Permutation, Parametric and Bootstrap Tests of Hypotheses: A Practical Guide to Resampling Methods for Testing Hypotheses by Phillip I. Good is a nonfiction available to read on EtoBox.
What is Permutation, Parametric and Bootstrap Tests of Hypotheses: A Practical Guide to Resampling Methods for Testing Hypotheses about?
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 world or who will be participating in the training of real-world statisticians and biostatisticians. In previous editions of this text, my rhetoric was somewhat tentative. I was saying, in effect. ''Gee guys, permutation methods provide a practical real-world alternative to asymptotic parametric approximations. YVhv not give them a try?'' But todav. the theorv. the software, and the liardware 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.
Who reads Permutation, Parametric and Bootstrap Tests of Hypotheses: A Practical Guide to Resampling Methods for Testing Hypotheses?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Phillip I. Good
- Publisher
- Springer US
- Published
- 2005
- Language
- EN
- ISBN
- 9781441919076
- Category
- nonfiction
- Subjects
- Mathematics, Science, Stem
Other editions & translations
- Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics) (2004)
- Permutation, Parametric and Bootstrap Tests of Hypotheses: A Practical Guide to Resampling Methods for Testing Hypotheses (2004)
- Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics) (2005)
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Browse all works by Phillip I. Good
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