Ronald W. Butler

Saddlepoint Approximations with Applications

• Gebundenes Buch

Produktbeschreibung

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.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 578
• Erscheinungstermin: 23. Februar 2015
• Englisch
• Abmessung: 260mm x 183mm x 35mm
• Gewicht: 1268g
• ISBN-13: 9780521872508
• ISBN-10: 0521872502
• Artikelnr.: 21220713

Autorenporträt

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.

Inhaltsangabe

Preface
1. Fundamental approximations
2. Properties and derivatives
3. Multivariate densities
4. Conditional densities and distribution functions
5. Exponential families and tilted distributions
6. Further exponential family examples and theory
7. Probability computation with p*
8. Probabilities with r*-type approximations
9. Nuisance parameters
10. Sequential saddlepoint applications
11. Applications to multivariate testing
12. Ratios and roots of estimating equations
13. First passage and time to event distributions
14. Bootstrapping in the transform domain
15. Bayesian applications
16. Non-normal bases
References
Index.