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This book explains how approximate probability calculations make complex models tractable, with clear, simple explanations and real data examples.
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Ronald W. Butler is Professor of Statistics at Colorado State University. His statistical work on prediction, likelihood methods, and saddlepoint methods is widely known. His more recent work concerns the study of complex stochastic systems, reliability, and survival analysis, with applications to electrical engineering and medical statistics. In applied mathematics, he has made important contributions to the approximation of hypergeometric functions with matrix and vector arguments.
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Saddlepoint Approximations with Applications
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Cambridge University Press, 2007
ISBN 10: 0521872502
ISBN 13: 9780521872508
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Saddlepoint Approximations with Applications
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Saddlepoint Approximations with Applications (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 22)
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Saddlepoint Appoximations with Applications
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Saddlepoint Approximations with Applications (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 22)
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Saddlepoint Approximations With Applications
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Cambridge University Press, Cambridge, 2007
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Hardcover. Condizione: new. Hardcover. Modern statistical methods use complex, sophisticated models that can lead to intractable computations. Saddlepoint approximations can be the answer. Written from the user's point of view, this book explains in clear language how such approximate probability computations are made, taking readers from the very beginnings to current applications. The core material is presented in chapters 1-6 at an elementary mathematical level. Chapters 7-9 then give a highly readable account of higher-order asymptotic inference. Later chapters address areas where saddlepoint methods have had substantial impact: multivariate testing, stochastic systems and applied probability, bootstrap implementation in the transform domain, and Bayesian computation and inference. No previous background in the area is required. Data examples from real applications demonstrate the practical value of the methods. Ideal for graduate students and researchers in statistics, biostatistics, electrical engineering, econometrics, and applied mathematics, this is both an entry-level text and a valuable reference. Modern statistical methods use complex, sophisticated models that can lead to intractable computations. Saddlepoint approximations can be the answer. Written from the user??'s point of view, this book explains in clear language how such approximate probability computations are made, taking readers from the very beginnings to current applications. The core material is presented in chapters 1-6 at an elementary mathematical level. Chapters 7-9 then give a highly readable account of higher-order asymptotic inference. Later chapters address areas where saddlepoint methods have had substantial impact: multivariate testing, stochastic systems and applied probability, bootstrap implementation in the transform domain, and Bayesian computation and inference. No previous background in the area is required. Data examples from real applications demonstrate the practical value of the methods. Ideal for graduate students and researchers in statistics, biostatistics, electrical engineering, econometrics, and applied mathematics, this is both an entry-level text and a valuable reference. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780521872508
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