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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.
Method of steepest descent (Numerical analysis) --- Method of steepest descent (Numerical analysis). --- Distribution (Probability theory) --- Approximations, Saddlepoint --- Descent, Method of steepest (Numerical analysis) --- Method of saddle points (Numerical analysis) --- Saddle point method (Numerical analysis) --- Saddle points, Method of (Numerical analysis) --- Saddlepoint approximations --- Saddlepoint method (Numerical analysis) --- Steepest descent method (Numerical analysis) --- Approximation theory --- Asymptotic expansions --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities
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Applied functional analysis has an extensive history. In the last century, this field has often been used in physical sciences, such as wave and heat phenomena. In recent decades, with the development of nonlinear functional analysis, this field has been used to model a variety of engineering, medical, and computer sciences. Two of the most significant issues in this area are modeling and optimization. Thus, we consider some recently published works on fixed point, variational inequalities, and optimization problems. These works could lead readers to obtain new novelties and familiarize them with some applications of this area.
vector variational-like inequalities --- vector optimization problems --- limiting (p,r)-α-(η,θ)-invexity --- Lipschitz continuity --- Fan-KKM theorem --- set-valued optimization problems --- higher-order weak adjacent epiderivatives --- higher-order mond-weir type dual --- benson proper efficiency --- fractional calculus --- ψ-fractional integrals --- fractional differential equations --- contraction --- hybrid contractions --- volterra fractional integral equations --- fixed point --- inertial-like subgradient-like extragradient method with line-search process --- pseudomonotone variational inequality problem --- asymptotically nonexpansive mapping --- strictly pseudocontractive mapping --- sequentially weak continuity --- method with line-search process --- pseudomonotone variational inequality --- strictly pseudocontractive mappings --- common fixed point --- hyperspace --- informal open sets --- informal norms --- null set --- open balls --- modified implicit iterative methods with perturbed mapping --- pseudocontractive mapping --- strongly pseudocontractive mapping --- nonexpansive mapping --- weakly continuous duality mapping --- set optimization --- set relations --- nonlinear scalarizing functional --- algebraic interior --- vector closure --- conjugate gradient method --- steepest descent method --- hybrid projection --- shrinking projection --- inertial Mann --- strongly convergence --- strict pseudo-contraction --- variational inequality problem --- inclusion problem --- signal processing
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