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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Libro 58 di 69: Statistics for Biology and HealthDa: Zubal-Books, Since 1961, Cleveland, OH, U.S.A.
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Condizione: New. 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 (Statistics for Biology and Health)
Libro 58 di 69: Statistics for Biology and HealthDa: Ria Christie Collections, Uxbridge, Regno Unito
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Libro 58 di 69: Statistics for Biology and HealthDa: Ria Christie Collections, Uxbridge, Regno Unito
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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
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Adaptive Regression for Modeling Nonlinear Relationships
Libro 58 di 69: Statistics for Biology and HealthDa: GreatBookPricesUK, Woodford Green, Regno Unito
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Libro 58 di 69: Statistics for Biology and HealthDa: Books Puddle, New York, NY, U.S.A.
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Libro 58 di 69: Statistics for Biology and HealthDa: Books Puddle, New York, NY, U.S.A.
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Da: Books From California, Simi Valley, CA, U.S.A.
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Adaptive Regression for Modeling Nonlinear Relationships
Libro 58 di 69: Statistics for Biology and HealthDa: Revaluation Books, Exeter, Regno Unito
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Adaptive Regression for Modeling Nonlinear Relationships
Libro 58 di 69: Statistics for Biology and HealthDa: GreatBookPricesUK, Woodford Green, Regno Unito
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Adaptive Regression for Modeling Nonlinear Relationships
Libro 58 di 69: Statistics for Biology and HealthEUR 68,25
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Adaptive Regression for Modeling Nonlinear Relationships | George J. Knafl (u. a.) | Taschenbuch | Statistics for Biology and Health | xxv | Englisch | 2018 | Springer | EAN 9783319816388 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Libro 58 di 69: Statistics for Biology and HealthDa: Mispah books, Redhill, SURRE, Regno Unito
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Adaptive Regression for Modeling Nonlinear Relationships
Libro 58 di 69: Statistics for Biology and HealthDa: GreatBookPrices, Columbia, MD, U.S.A.
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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
Da: Ria Christie Collections, Uxbridge, Regno Unito
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Libro 58 di 69: Statistics for Biology and HealthDa: Mispah books, Redhill, SURRE, Regno Unito
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2024
ISBN 10: 3031419871 ISBN 13: 9783031419874
Da: Rarewaves.com USA, London, LONDO, Regno Unito
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Aggiungi al carrelloHardback. Condizione: New. 2023 ed. 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 : With Online Files
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling : With Online Files
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Aggiungi al carrelloCondizione: Sehr gut. Zustand: Sehr gut | Seiten: 544 | Sprache: Englisch | Produktart: Bücher | 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
Da: Books Puddle, New York, NY, U.S.A.
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling : With Online Files
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 131,05
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling | George J. Knafl | Taschenbuch | xxv | Englisch | 2025 | Springer | EAN 9783031419904 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling : With Online Files
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 197,65
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Lingua: Inglese
Editore: Springer International Publishing, Springer Nature Switzerland, 2025
ISBN 10: 3031419901 ISBN 13: 9783031419904
EUR 149,79
Spedizione EUR 64,07
Spedito da Germania a U.S.A.Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: 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
Da: Books Puddle, New York, NY, U.S.A.
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