Applied Asymptotics: Case Studies in Small-Sample Statistics: 23 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 23) - Hardcover
Book 15 of 45: Cambridge Series in Statistical and Probabilistic Mathematics
Synopsis
First practical treatment of small-sample asymptotics, enabling practitioners to apply new methods with confidence.
"synopsis" may belong to another edition of this title.
About the Authors
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.
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Applied Asymptotics : Case Studies in Small-Sample Statistics
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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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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/ This item is printed on demand. 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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