02788nam a22003138i 4500001001600000003000700016008004100023020001800064020001800082035002000100041000800120082001400128100003100142245008000173264007100253300003800324336002600362337002600388338003600414490007200450500001300522505059800535520109101133650005202224856005502276932003202331596000602363949010502369CR9780511619083UkCbUP090915s2007||||enk o ||1 0|eng|d a9780511619083 a9780521872508 a(Sirsi) a791200 aeng04a511.42221 aButler, Ronald W.,eauthor10aSaddlepoint approximations with applicationsh[E-Book] /cRonald W. Butler. 1aCambridge :bCambridge University Press,c2007e(CUP)fCUP20200108 a1 online resource (xi, 564 pages) atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier aCambridge series on statistical and probabilistic mathematics ;v22 aenglisch0 aFundamental 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. aModern 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. 0aMethod of steepest descent (Numerical analysis)40uhttps://doi.org/10.1017/CBO9780511619083zVolltext aCambridgeCore (Order 30059) a1 aXX(791200.1)wAUTOc1i791200-1001lELECTRONICmZBrNsYtE-BOOKu8/1/2020xUNKNOWNzUNKNOWN1ONLINE