Adaptive Regression For Modeling Nonlinear Relationships
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Adaptive Regression For Modeling Nonlinear Relationships
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Author : George J. Knafl
language : en
Publisher: Springer
Release Date : 2016-09-20
Adaptive Regression For Modeling Nonlinear Relationships written by George J. Knafl and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-20 with Medical categories.
This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the standard, logistic, and Poisson regression contexts with continuous, discrete, and counts outcomes, respectively, either univariate or multivariate. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes. The authors have created customized SAS macros for use in conducting adaptive regression modeling. These macros and code for conducting the analyses discussed in the book are available through the first author's website and online via the book’s Springer website. Detailed descriptions of how to use these macros and interpret their output appear throughout the book. These methods can be implemented using other programs.
Adaptive Regression
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Author : Yadolah Dodge
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-10-01
Adaptive Regression written by Yadolah Dodge 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 2012-10-01 with Mathematics categories.
While there have been a large number of estimation methods proposed and developed for linear regression, none has proved good for all purposes. This text focuses on the construction of an adaptive combination of two estimation methods so as to help users make an objective choice and combine the desirable properties of two estimators.
Handbook Of Nonlinear Regression Models
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Author : David A. Ratkowsky
language : en
Publisher:
Release Date : 1990
Handbook Of Nonlinear Regression Models written by David A. Ratkowsky and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Mathematics categories.
The background; An introduction to regression modeling; Nonlinear regression modeling; An illustrative example of regression modeling; The models; Models with one X variable, convex/concave curves; Models with one X variable, sigmoidally shaped curves; Models with one X variable, curves with maxima and minima; Models with more than one explanatory viariable; Other models and excluded models; Obtaining good initial parameter estimates; Summary; References; Table of symbols; Appendix; Author index; Subject index.
Nonlinear Regression Modeling
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Author : David A. Ratkowsky
language : en
Publisher:
Release Date : 1983
Nonlinear Regression Modeling written by David A. Ratkowsky and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Mathematics categories.
Introduction to regression models; Assessing nonlinearity in nonlinear regression models; Yield-density models; Sigmoidal growth models; Asymptotic regression model; Some miscellaneous models; Comparing parameter estimates from more than one data set; Obtaining good initial parameter estimates; Summary: towatd a unified approach to nonlinear regression modeling.
Nonlinear Regression Modeling
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Author : David A. Ratkowsky
language : en
Publisher:
Release Date : 1983
Nonlinear Regression Modeling written by David A. Ratkowsky and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with categories.
Nonlinear Regression Modeling For Engineering Applications
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Author : R. Russell Rhinehart
language : en
Publisher: John Wiley & Sons
Release Date : 2016-09-26
Nonlinear Regression Modeling For Engineering Applications written by R. Russell Rhinehart and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-26 with Mathematics categories.
Since mathematical models express our understanding of how nature behaves, we use them to validate our understanding of the fundamentals about systems (which could be processes, equipment, procedures, devices, or products). Also, when validated, the model is useful for engineering applications related to diagnosis, design, and optimization. First, we postulate a mechanism, then derive a model grounded in that mechanistic understanding. If the model does not fit the data, our understanding of the mechanism was wrong or incomplete. Patterns in the residuals can guide model improvement. Alternately, when the model fits the data, our understanding is sufficient and confidently functional for engineering applications. This book details methods of nonlinear regression, computational algorithms,model validation, interpretation of residuals, and useful experimental design. The focus is on practical applications, with relevant methods supported by fundamental analysis. This book will assist either the academic or industrial practitioner to properly classify the system, choose between the various available modeling options and regression objectives, design experiments to obtain data capturing critical system behaviors, fit the model parameters based on that data, and statistically characterize the resulting model. The author has used the material in the undergraduate unit operations lab course and in advanced control applications.
Nonlinear Regression Modeling
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Author : David A. Ratkowsky
language : en
Publisher:
Release Date : 1986
Nonlinear Regression Modeling written by David A. Ratkowsky and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with categories.
Statistical Concepts A Second Course
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Author : Debbie L. Hahs-Vaughn
language : en
Publisher: Routledge
Release Date : 2020-01-17
Statistical Concepts A Second Course written by Debbie L. Hahs-Vaughn and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-17 with Psychology categories.
Statistical Concepts—A Second Course presents the last 10 chapters from An Introduction to Statistical Concepts, Fourth Edition. Designed for second and upper-level statistics courses, this book highlights how statistics work and how best to utilize them to aid students in the analysis of their own data and the interpretation of research results. In this new edition, Hahs-Vaughn and Lomax discuss sensitivity, specificity, false positive and false negative errors. Coverage of effect sizes has been expanded upon and more organizational features (to summarize key concepts) have been included. A final chapter on mediation and moderation has been added for a more complete presentation of regression models. In addition to instructions and screen shots for using SPSS, new to this edition is annotated script for using R. This book acts as a clear and accessible instructional tool to help readers fully understand statistical concepts and how to apply them to data. It is an invaluable resource for students undertaking a course in statistics in any number of social science and behavioral science disciplines.
Imputation Methods For Missing Hydrometeorological Data Estimation
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Author : Ramesh S. V. Teegavarapu
language : en
Publisher: Springer Nature
Release Date :
Imputation Methods For Missing Hydrometeorological Data Estimation written by Ramesh S. V. Teegavarapu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Nonlinear Regression
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Author : George A. F. Seber
language : en
Publisher: John Wiley & Sons
Release Date : 2005-02-25
Nonlinear Regression written by George A. F. Seber and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-02-25 with Mathematics categories.
WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. From the Reviews of Nonlinear Regression "A very good book and an important one in that it is likely to become a standard reference for all interested in nonlinear regression; and I would imagine that any statistician concerned with nonlinear regression would want a copy on his shelves." –The Statistician "Nonlinear Regression also includes a reference list of over 700 entries. The compilation of this material and cross-referencing of it is one of the most valuable aspects of the book. Nonlinear Regression can provide the researcher unfamiliar with a particular specialty area of nonlinear regression an introduction to that area of nonlinear regression and access to the appropriate references . . . Nonlinear Regression provides by far the broadest discussion of nonlinear regression models currently available and will be a valuable addition to the library of anyone interested in understanding and using such models including the statistical researcher." –Mathematical Reviews