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Applied Asymptotics: Case Studies in Small-Sample Statistics (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 23) - Hardcover
Synopsis
In fields such as biology, medical sciences, sociology, and economics 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 normal 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 resource page: http://www.isib.cnr.it/~brazzale/AA/
"synopsis" may belong to another edition of this title.
Book Description
New likelihood-based methods now allow very accurate approximations in finite samples and this book illustrates with realistic examples and case studies how to use the new theory. The treatment is oriented towards practice and is accompanied by code in the R language, enabling practitioners to apply the methods with ease.
About the Author
Alessandra 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 Fédérale de Lausanne.
Nancy Reid is a University Professor of Statistics at the University of Toronto.
"About this title" may belong to another edition of this title.
- PublisherCambridge University Press
- Publication date2007
- ISBN 10 0521847036
- ISBN 13 9780521847032
- BindingHardcover
- LanguageEnglish
- Number of pages248
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Applied Asymptotics : Case Studies in Small-Sample Statistics
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Applied asymptotics. Case studies in small-sample statistics - A.R. Brazzale
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Applied Asymptotics: Case Studies in Small-Sample Statistics: 23 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 23)
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Applied Asymptotics: Case Studies in Small-Sample Statistics: 23 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 23)
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Applied Asymptotics: Case Studies in Small-Sample Statistics
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Applied Asymptotics: Case Studies in Small-Sample Statistics (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 23)
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Applied Asymptotics: Case Studies in Small-Sample Statistics
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Applied Asymptotics: Case Studies in Small-Sample Statistics (Hardcover)
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Hardcover. Condition: new. Hardcover. In fields such as biology, medical sciences, sociology, and economics 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 normal 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 resource page: / brazzale/AA/ In fields such as biology, medical sciences, sociology, and economics 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 normal 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 resource page: ~brazzale/AA/ Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521847032
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Applied Asymptotics Case Studies in Small-Sample Statistics
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ISBN 13: 9780521847032
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