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Saddlepoint approximations with applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Saddlepoint approximations with applications // Ronald W. Butler.
作者:	Butler, Ronald W.,
面頁冊數:	1 online resource (xi, 564 pages) :digital, PDF file(s). :
附註:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
標題:	Method of steepest descent (Numerical analysis) -
電子資源:	https://doi.org/10.1017/CBO9780511619083
ISBN:	9780511619083 (ebook)

Saddlepoint approximations with applications /
Butler, Ronald W.,

Saddlepoint approximations with applications /Ronald W. Butler. - 1 online resource (xi, 564 pages) :digital, PDF file(s). - Cambridge series on statistical and probabilistic mathematics ;22. - Cambridge series on statistical and probabilistic mathematics ;32..

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Fundamental approximations -- Properties and derivations -- Multivariate densities -- Conditional densities and distribution functions -- Exponential families and tilted distributions -- Further exponential family examples and theory -- Probability computation with p* -- Probabilities with r*-type approximations -- Nuisance parameters -- Sequential saddlepoint applications -- Applications to multivariate testing -- Ratios and roots of estimating equations -- First passge and time to event distributions -- Bootstrapping in the transform domain -- Bayesian applications -- Nonnormal bases.

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.

ISBN: 9780511619083 (ebook)Subjects--Topical Terms:

1211313
Method of steepest descent (Numerical analysis)

LC Class. No.: QA297.5 / .B885 2007

Dewey Class. No.: 511.4

Saddlepoint approximations with applications /
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505 0 $a Fundamental approximations -- Properties and derivations -- Multivariate densities -- Conditional densities and distribution functions -- Exponential families and tilted distributions -- Further exponential family examples and theory -- Probability computation with p* -- Probabilities with r*-type approximations -- Nuisance parameters -- Sequential saddlepoint applications -- Applications to multivariate testing -- Ratios and roots of estimating equations -- First passge and time to event distributions -- Bootstrapping in the transform domain -- Bayesian applications -- Nonnormal bases.
520 $a 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.
650 0 $a Method of steepest descent (Numerical analysis) $3 1211313
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830 0 $a Cambridge series on statistical and probabilistic mathematics ; $v 32. $3 1133567
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