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- DescriptionIn fields such as biology, medical sciences, sociology, and ecomics researchers often face the situation where the number of available observations, or the amount of available information, is sufficiently small that approximations based on the rmal distribution may be unreliable. Theoretical work over the last quarter-century has led to new likelihood-based methods that lead to very accurate approximations in finite samples, but this work has had limited impact on statistical practice. This book illustrates by means of realistic examples and case studies how to use the new theory, and investigates how and when it makes a difference to the resulting inference. The treatment is oriented towards practice and comes with code in the R language (available from the web) which enables the methods to be applied in a range of situations of interest to practitioners. The analysis includes some comparisons of higher order likelihood inference with bootstrap or Bayesian methods.
- Author BiographyAlessandra Brazzale is a Researcher in Statistics at the Institute of Biomedical Engineering, Italian National Research Council, Padova. Anthony Davison is a Professor of Statistics at the Ecole Polytechnique Federale de Lausanne. Nancy Reid is a University Professor of Statistics at the University of Toronto.
- Author(s)A. C. Davison,A. R. Brazzale,N. Reid
- PublisherCambridge University Press
- Date of Publication31/05/2007
- SubjectScience & Mathematics: Textbooks & Study Guides
- Series TitleCambridge Series in Statistical and Probabilistic Mathematics
- Series Part/Volume Numberv .23
- Place of PublicationCambridge
- Country of PublicationUnited Kingdom
- ImprintCambridge University Press
- Content Note49 b/w illus. 47 tables 69 exercises
- Weight604 g
- Width278 mm
- Height254 mm
- Spine18 mm
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