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Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 41) - Hardcover
Synopsis
This lively book lays out a methodology of confidence distributions and puts them through their paces. Among other merits they lead to optimal combinations of confidence from different sources of information, and they can make complex models amenable to objective and indeed prior-free analysis for less subjectively inclined statisticians. The generous mixture of theory, illustrations, applications and exercises is suitable for statisticians at all levels of experience, as well as for data-oriented scientists. Some confidence distributions are less dispersed than their competitors. This concept leads to a theory of risk functions and comparisons for distributions of confidence. Neyman-Pearson type theorems leading to optimal confidence are developed and richly illustrated. Exact and optimal confidence distribution is the gold standard for inferred epistemic distributions. Confidence distributions and likelihood functions are intertwined, allowing prior distributions to be made part of the likelihood. Meta-analysis in likelihood terms is developed and taken beyond traditional methods, suiting it in particular to combining information across diverse data sources.
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About the Authors
Tore Schweder is a Professor of Statistics in the Department of Economics and at the Centre for Ecology and Evolutionary Synthesis at the University of Oslo.
Nils Lid Hjort is Professor of Mathematical Statistics in the Department of Mathematics at the University of Oslo.
"About this title" may belong to another edition of this title.
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Confidence, Likelihood ,and Probability : Statistical Inference With Confidence Distributions
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Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 41)
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Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 41)
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Confidence, Likelihood, Probability (Hardcover)
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Hardcover. Condition: new. Hardcover. This lively book lays out a methodology of confidence distributions and puts them through their paces. Among other merits, they lead to optimal combinations of confidence from different sources of information, and they can make complex models amenable to objective and indeed prior-free analysis for less subjectively inclined statisticians. The generous mixture of theory, illustrations, applications and exercises is suitable for statisticians at all levels of experience, as well as for data-oriented scientists. Some confidence distributions are less dispersed than their competitors. This concept leads to a theory of risk functions and comparisons for distributions of confidence. NeymanPearson type theorems leading to optimal confidence are developed and richly illustrated. Exact and optimal confidence distribution is the gold standard for inferred epistemic distributions. Confidence distributions and likelihood functions are intertwined, allowing prior distributions to be made part of the likelihood. Meta-analysis in likelihood terms is developed and taken beyond traditional methods, suiting it in particular to combining information across diverse data sources. This is the first book to develop a methodology of confidence distributions, with a lively mix of theory, illustrations, applications and exercises. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521861601
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Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 41)
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Confidence, Likelihood ,and Probability: Statistical Inference With Confidence Distributions
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Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions
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Confidence, Likelihood, Probability (Hardcover)
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Hardcover. Condition: new. Hardcover. This lively book lays out a methodology of confidence distributions and puts them through their paces. Among other merits, they lead to optimal combinations of confidence from different sources of information, and they can make complex models amenable to objective and indeed prior-free analysis for less subjectively inclined statisticians. The generous mixture of theory, illustrations, applications and exercises is suitable for statisticians at all levels of experience, as well as for data-oriented scientists. Some confidence distributions are less dispersed than their competitors. This concept leads to a theory of risk functions and comparisons for distributions of confidence. NeymanPearson type theorems leading to optimal confidence are developed and richly illustrated. Exact and optimal confidence distribution is the gold standard for inferred epistemic distributions. Confidence distributions and likelihood functions are intertwined, allowing prior distributions to be made part of the likelihood. Meta-analysis in likelihood terms is developed and taken beyond traditional methods, suiting it in particular to combining information across diverse data sources. This is the first book to develop a methodology of confidence distributions, with a lively mix of theory, illustrations, applications and exercises. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9780521861601
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Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 41)
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Cambridge University Press, 2016
ISBN 10: 0521861608
ISBN 13: 9780521861601
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