Numerical Methods For Nonlinear Regression

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Numerical Methods For Nonlinear Regression

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Author : David Royce Sadler
language : en
Publisher:
Release Date : 1975

Numerical Methods For Nonlinear Regression written by David Royce Sadler and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1975 with Regression analysis categories.

Numerical Techniques Of Nonlinear Regression Model Estimation

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Author : Dr Ranadheer Donthi
language : en
Publisher:
Release Date : 2020

Numerical Techniques Of Nonlinear Regression Model Estimation written by Dr Ranadheer Donthi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

The literature on numerical methods for fitting nonlinear regression model has grown enormously in the fast five decades. An important phase in nonlinear regression problems is the exploration of the relation between the independent and dependent variables. A largely unexplored area of research in nonlinear regression models concerns the finite sample properties of nonlinear parameters. The main object of this research study is to pro- pose some nonlinear methods of estimation of nonlinear regression models, namely Newton- Raphson method, Gauss-Newton method, Method of scoring, Quadratic Hill-Climbing and Conjugate Gradient methods. In 2005, G.E. Hovland et al. In his research article, presented a parameter estimation of physical time-varying parameters for combined-cycle power plant models. B. Mahaboob et al. (see [6]), in their research paper, proposed some computational methods based on numerical analysis to estimate the parameters of nonlinear regression model. S.J. Juliear et al., in their research paper, developed the method of unscented transformation (UT) to propagate mean and covariance information through nonlinear transformations.

Numerical Methods For Nonlinear Partial Differential Equations

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Author : Sören Bartels
language : en
Publisher: Springer
Release Date : 2015-01-19

Numerical Methods For Nonlinear Partial Differential Equations written by Sören Bartels and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-19 with Mathematics categories.

The description of many interesting phenomena in science and engineering leads to infinite-dimensional minimization or evolution problems that define nonlinear partial differential equations. While the development and analysis of numerical methods for linear partial differential equations is nearly complete, only few results are available in the case of nonlinear equations. This monograph devises numerical methods for nonlinear model problems arising in the mathematical description of phase transitions, large bending problems, image processing, and inelastic material behavior. For each of these problems the underlying mathematical model is discussed, the essential analytical properties are explained, and the proposed numerical method is rigorously analyzed. The practicality of the algorithms is illustrated by means of short implementations.

Numerical Methods For Nonlinear Estimating Equations

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Author : Christopher G. Small
language : en
Publisher: OUP Oxford
Release Date : 2003-10-02

Numerical Methods For Nonlinear Estimating Equations written by Christopher G. Small and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-10-02 with Mathematics categories.

Nonlinearity arises in statistical inference in various ways, with varying degrees of severity, as an obstacle to statistical analysis. More entrenched forms of nonlinearity often require intensive numerical methods to construct estimators, and the use of root search algorithms, or one-step estimators, is a standard method of solution. This book provides a comprehensive study of nonlinear estimating equations and artificial likelihoods for statistical inference. It provides extensive coverage and comparison of hill climbing algorithms, which, when started at points of nonconcavity often have very poor convergence properties, and for additional flexibility proposes a number of modifications to the standard methods for solving these algorithms. The book also extends beyond simple root search algorithms to include a discussion of the testing of roots for consistency, and the modification of available estimating functions to provide greater stability in inference. A variety of examples from practical applications are included to illustrate the problems and possibilities thus making this text ideal for the research statistician and graduate student. This is the latest in the well-established and authoritative Oxford Statistical Science Series, which includes texts and monographs covering many topics of current research interest in pure and applied statistics. Each title has an original slant even if the material included is not specifically original. The authors are leading researchers and the topics covered will be of interest to all professional statisticians, whether they be in industry, government department or research institute. Other books in the series include 23. W.J.Krzanowski: Principles of multivariate analysis: a user's perspective updated edition 24. J.Durbin and S.J.Koopman: Time series analysis by State Space Models 25. Peter J. Diggle, Patrick Heagerty, Kung-Yee Liang, Scott L. Zeger: Analysis of Longitudinal Data 2/e 26. J.K. Lindsey: Nonlinear Models in Medical Statistics 27. Peter J. Green, Nils L. Hjort & Sylvia Richardson: Highly Structured Stochastic Systems 28. Margaret S. Pepe: The Statistical Evaluation of Medical Tests for Classification and Prediction

Numerical Methods For Nonlinear Engineering Models

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Author : John R. Hauser
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-03-24

Numerical Methods For Nonlinear Engineering Models written by John R. Hauser and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-03-24 with Technology & Engineering categories.

There are many books on the use of numerical methods for solving engineering problems and for modeling of engineering artifacts. In addition there are many styles of such presentations ranging from books with a major emphasis on theory to books with an emphasis on applications. The purpose of this book is hopefully to present a somewhat different approach to the use of numerical methods for - gineering applications. Engineering models are in general nonlinear models where the response of some appropriate engineering variable depends in a nonlinear manner on the - plication of some independent parameter. It is certainly true that for many types of engineering models it is sufficient to approximate the real physical world by some linear model. However, when engineering environments are pushed to - treme conditions, nonlinear effects are always encountered. It is also such - treme conditions that are of major importance in determining the reliability or failure limits of engineering systems. Hence it is essential than engineers have a toolbox of modeling techniques that can be used to model nonlinear engineering systems. Such a set of basic numerical methods is the topic of this book. For each subject area treated, nonlinear models are incorporated into the discussion from the very beginning and linear models are simply treated as special cases of more general nonlinear models. This is a basic and fundamental difference in this book from most books on numerical methods.

Fitting Models To Biological Data Using Linear And Nonlinear Regression

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Author : Harvey Motulsky
language : en
Publisher: Oxford University Press
Release Date : 2004-05-27

Fitting Models To Biological Data Using Linear And Nonlinear Regression written by Harvey Motulsky and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-05-27 with Mathematics categories.

Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.

Numerical Methods Of Statistics

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Author : John F. Monahan
language : en
Publisher: Cambridge University Press
Release Date : 2001-02-05

Numerical Methods Of Statistics written by John F. Monahan and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-02-05 with Computers categories.

This 2001 book provides a basic background in numerical analysis and its applications in statistics.

Fast Numerical Methods For Mixed Integer Nonlinear Model Predictive Control

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Author : Christian Kirches
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-11-23

Fast Numerical Methods For Mixed Integer Nonlinear Model Predictive Control written by Christian Kirches and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-11-23 with Computers categories.

Christian Kirches develops a fast numerical algorithm of wide applicability that efficiently solves mixed-integer nonlinear optimal control problems. He uses convexification and relaxation techniques to obtain computationally tractable reformulations for which feasibility and optimality certificates can be given even after discretization and rounding.

Numerical Methods For Nonlinear Variational Problems

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Author : Roland Glowinski
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29

Numerical Methods For Nonlinear Variational Problems written by Roland Glowinski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Science categories.

This book describes the mathematical background and reviews the techniques for solving problems, including those that require large computations such as transonic flows for compressible fluids and the Navier-Stokes equations for incompressible viscous fluids. Finite element approximations and non-linear relaxation, and nonlinear least square methods are all covered in detail, as are many applications. This volume is a classic in a long-awaited softcover re-edition.

Numerical Methods For Unconstrained Optimization And Nonlinear Equations

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Author : J. E. Dennis, Jr.
language : en
Publisher: SIAM
Release Date : 1996-12-01

Numerical Methods For Unconstrained Optimization And Nonlinear Equations written by J. E. Dennis, Jr. and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-12-01 with Mathematics categories.

This book has become the standard for a complete, state-of-the-art description of the methods for unconstrained optimization and systems of nonlinear equations. Originally published in 1983, it provides information needed to understand both the theory and the practice of these methods and provides pseudocode for the problems. The algorithms covered are all based on Newton's method or "quasi-Newton" methods, and the heart of the book is the material on computational methods for multidimensional unconstrained optimization and nonlinear equation problems. The republication of this book by SIAM is driven by a continuing demand for specific and sound advice on how to solve real problems. The level of presentation is consistent throughout, with a good mix of examples and theory, making it a valuable text at both the graduate and undergraduate level. It has been praised as excellent for courses with approximately the same name as the book title and would also be useful as a supplemental text for a nonlinear programming or a numerical analysis course. Many exercises are provided to illustrate and develop the ideas in the text. A large appendix provides a mechanism for class projects and a reference for readers who want the details of the algorithms. Practitioners may use this book for self-study and reference. For complete understanding, readers should have a background in calculus and linear algebra. The book does contain background material in multivariable calculus and numerical linear algebra.