Articles liés à Adaptive Regression for Modeling Nonlinear Relationships
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Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
George Knafl is Professor and Biostatistician in the School of Nursing of the University of North Carolina at Chapel Hill where he teaches statistics courses to doctoral nursing students, consults with graduate students and faculty on their research, and conducts his own research. He has over 35 years of experience in teaching, consulting, and research in statistics. His research involves development of methods for searching through alternative models for data to identify an effective choice for modeling those data and the application of those methods to the analysis of health science data sets. He is also Professor Emeritus in the College of Computing and Digital Media at DePaul University and has also taught in Schools of Nursing at Yale University and the Oregon Health and Sciences University.
Kai Ding is Assistant Professor, Department of Biostatistics and Epidemiology at the University of Oklahoma (OU) Health Sciences Center. He is also Associated Member ofthe Peggy and Charles Stephenson Cancer Center (SCC) of OU Medicine. Dr. Ding received his Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill in 2010. His research has focuses on survival analysis and semiparametric inference. He has been involved in the design and analysis of numerous research studies in cancer and ophthalmology and currently serves on the Scientific Review Committee and the Protocol Monitoring Committee of the SCC.
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Adaptive Regression for Modeling Nonlinear Relationships
Edité par
Springer International Publishing, 2018
ISBN 10 : 3319816381
ISBN 13 : 9783319816388
Neuf
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Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides insight into modeling of nonlinear relationships and also justifications for when to use them, thereby providing novel insights about relationshipsAddresses not only adaptive generation of additive models but also of . N° de réf. du vendeur 385706014
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Adaptive Regression for Modeling Nonlinear Relationships
ISBN 10 : 3319816381
ISBN 13 : 9783319816388
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783319816388
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Adaptive Regression for Modeling Nonlinear Relationships
Edité par
Springer International Publishing Jun 2018, 2018
ISBN 10 : 3319816381
ISBN 13 : 9783319816388
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 400 pp. Englisch. N° de réf. du vendeur 9783319816388
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Vendeur : Chiron Media, Wallingford, Royaume-Uni
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Paperback. Etat : New. N° de réf. du vendeur 6666-IUK-9783319816388
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Adaptive Regression for Modeling Nonlinear Relationships
ISBN 10 : 3319816381
ISBN 13 : 9783319816388
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 400 pp. Englisch. N° de réf. du vendeur 9783319816388
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Vendeur : Books Puddle, New York, NY, Etats-Unis
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Etat : New. pp. 372. N° de réf. du vendeur 26381427785
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Knafl:Adaptive Regression for Modeling (eng)
impression à la demandeVendeur : Brook Bookstore On Demand, Napoli, NA, Italie
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Etat : new. Questo è un articolo print on demand. N° de réf. du vendeur 1b0198c1a579c49c722fc570936898a9
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
impression à la demandeVendeur : Majestic Books, Hounslow, Royaume-Uni
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Etat : New. Print on Demand pp. 372. N° de réf. du vendeur 382443414
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
impression à la demandeVendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Etat : New. PRINT ON DEMAND pp. 372. N° de réf. du vendeur 18381427779
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