Adaptive Regression for Modeling Nonlinear Relationships

Inbunden, Engelska, 2016

Av George J. Knafl, Kai Ding

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This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes.

Produktinformation

Utgivningsdatum2016-09-30
Mått155 x 235 x 24 mm
Vikt535 g
FormatInbunden
SpråkEngelska
SerieStatistics for Biology and Health
Antal sidor372
FörlagSpringer International Publishing AG
ISBN9783319339443

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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.

Introduction.- Adaptive Regression Modeling of Univariate Continuous Outcomes.- Adaptive Regression Modeling of Univariate Continuous Outcomes in SAS.- Adaptive Regression Modeling of Multivariate Continuous Outcomes.- Adaptive Regression Modeling of Multivariate Continuous Outcomes in SAS.- Adaptive Transformation of Positive Valued Continuous Outcomes.- Adaptive Logistic Regression Modeling of Univariate Dichotomous and Polytomous Outcomes.- Adaptive Logistic Regression Modeling of Univariate Dichotomous and Polytomous Outcomes in SAS.- Adaptive Logistic Regression Modeling of Multivariate Dichotomous and Polytomous Outcomes.- Adaptive Logistic Regression Modeling of Multivariate Dichotomous and Polytomous Outcomes in SAS.- Adaptive Poisson Regression Modeling of Univariate Count Outcomes.- Adaptive Poisson Regression Modeling of Univariate Count Outcomes in SAS.- Adaptive Poisson Regression Modeling of Multivariate Count Outcomes.- Adaptive Poisson Regression Modeling of Multivariate Count Outcomes in SAS.- Generalized Additive Modeling.- Generalized Additive Modeling in SAS.- Multivariate Adaptive Regression Spline Modeling.- Multivariate Adaptive Regression Spline Modeling in SAS.- Adaptive Regression Modeling Formulation.