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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
First Edition
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Seller: Zubal-Books, Since 1961, Cleveland, OH, U.S.A.
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Add to basketCondition: New. *Price HAS BEEN temporarily REDUCED by 10% until Monday, Sept. 15 (sale item)* 372 pp., hardcover, new. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.
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Adaptive Regression for Modeling Nonlinear Relationships
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
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Adaptive Regression for Modeling Nonlinear Relationships
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Published by Springer International Publishing, Springer Nature Switzerland, 2025
ISBN 10: 3031419901 ISBN 13: 9783031419904
Language: English
£ 133.35
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book formulates methods for modeling continuous and categorical correlated outcomes that extend the commonly used methods: generalized estimating equations (GEE) and linear mixed modeling. Partially modified GEE adds estimating equations for variance/dispersion parameters to the standard GEE estimating equations for the mean parameters. Fully modified GEE provides alternate estimating equations for mean parameters as well as estimating equations for variance/dispersion parameters. The new estimating equations in these two cases are generated by maximizing a 'likelihood' function related to the multivariate normal density function. Partially modified GEE and fully modified GEE use the standard GEE approach to estimate correlation parameters based on the residuals. Extended linear mixed modeling (ELMM) uses the likelihood function to estimate not only mean and variance/dispersion parameters, but also correlation parameters. Formulations are provided for gradient vectors and Hessianmatrices, for a multi-step algorithm for solving estimating equations, and model-based and robust empirical tests for assessing theory-based models.Standard GEE, partially modified GEE, fully modified GEE, and ELMM are demonstrated and compared using a variety of regression analyses of different types of correlated outcomes. Example analyses of correlated outcomes include linear regression for continuous outcomes, Poisson regression for count/rate outcomes, logistic regression for dichotomous outcomes, exponential regression for positive-valued continuous outcome, multinomial regression for general polytomous outcomes, ordinal regression for ordinal polytomous outcomes, and discrete regression for discrete numeric outcomes. These analyses also address nonlinearity in predictors based on adaptive search through alternative fractional polynomial models controlled by likelihood cross-validation (LCV) scores. Larger LCV scores indicate better models but not necessarilydistinctly better models. LCV ratio tests are used to identify distinctly better models.A SAS macro has been developed for analyzing correlated outcomes using standard GEE, partially modified GEE, fully modified GEE, and ELMM within alternative regression contexts. This macro and code for conducting the analyses addressed in the book are available online via the book's Springer website. Detailed descriptions of how to use this macro and interpret its output are provided in the book.
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Published by Springer International Publishing, Springer Nature Switzerland, 2024
ISBN 10: 3031419871 ISBN 13: 9783031419874
Language: English
£ 133.35
Convert currency£ 12.09 shipping from Germany to United KingdomQuantity: 1 available
Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book formulates methods for modeling continuous and categorical correlated outcomes that extend the commonly used methods: generalized estimating equations (GEE) and linear mixed modeling. Partially modified GEE adds estimating equations for variance/dispersion parameters to the standard GEE estimating equations for the mean parameters. Fully modified GEE provides alternate estimating equations for mean parameters as well as estimating equations for variance/dispersion parameters. The new estimating equations in these two cases are generated by maximizing a 'likelihood' function related to the multivariate normal density function. Partially modified GEE and fully modified GEE use the standard GEE approach to estimate correlation parameters based on the residuals. Extended linear mixed modeling (ELMM) uses the likelihood function to estimate not only mean and variance/dispersion parameters, but also correlation parameters. Formulations are provided for gradient vectors and Hessianmatrices, for a multi-step algorithm for solving estimating equations, and model-based and robust empirical tests for assessing theory-based models.Standard GEE, partially modified GEE, fully modified GEE, and ELMM are demonstrated and compared using a variety of regression analyses of different types of correlated outcomes. Example analyses of correlated outcomes include linear regression for continuous outcomes, Poisson regression for count/rate outcomes, logistic regression for dichotomous outcomes, exponential regression for positive-valued continuous outcome, multinomial regression for general polytomous outcomes, ordinal regression for ordinal polytomous outcomes, and discrete regression for discrete numeric outcomes. These analyses also address nonlinearity in predictors based on adaptive search through alternative fractional polynomial models controlled by likelihood cross-validation (LCV) scores. Larger LCV scores indicate better models but not necessarilydistinctly better models. LCV ratio tests are used to identify distinctly better models.A SAS macro has been developed for analyzing correlated outcomes using standard GEE, partially modified GEE, fully modified GEE, and ELMM within alternative regression contexts. This macro and code for conducting the analyses addressed in the book are available online via the book's Springer website. Detailed descriptions of how to use this macro and interpret its output are provided in the book.
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£ 133.01
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Add to basketCondition: As New. Unread book in perfect condition.
