Order Total (1 Item Items):
Shipping Destination:
Knafl George (45 results)
Skip to main search results
Search filters
Product Type
- All Product Types
- Books (45)
- Magazines & Periodicals (No further results match this refinement)
- Comics (No further results match this refinement)
- Sheet Music (No further results match this refinement)
- Art, Prints & Posters (No further results match this refinement)
- Photographs (No further results match this refinement)
- Maps (No further results match this refinement)
- Manuscripts & Paper Collectibles (No further results match this refinement)
Condition Learn more
- New (38)
- As New, Fine or Near Fine (6)
- Very Good or Good (1)
- Fair or Poor (No further results match this refinement)
- As Described (No further results match this refinement)
Binding
Collectible Attributes
- First Edition (No further results match this refinement)
- Signed (No further results match this refinement)
- Dust Jacket (No further results match this refinement)
- Seller-Supplied Images (19)
- Not Print on Demand (28)
Language (1)
Free Shipping
Seller Location
Seller Rating
-
Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Book 58 of 69: Statistics for Biology and HealthSeller: Zubal-Books, Since 1961, Cleveland, OH, U.S.A.
Seller rating 5 out of 5 stars
Condition: 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.
-
-
-
-
-
Adaptive Regression for Modeling Nonlinear Relationships
Book 58 of 69: Statistics for Biology and HealthSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 86.38
US$ 20.34 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New.
-
Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Book 58 of 69: Statistics for Biology and HealthSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 89.80
US$ 16.24 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
-
Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Book 58 of 69: Statistics for Biology and HealthUS$ 88.82
US$ 21.00 shipping
Ships from United Kingdom to U.S.A.Quantity: 10 available
Add to basketPaperback. Condition: New.
-
Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Book 58 of 69: Statistics for Biology and HealthSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 90.22
US$ 16.24 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
-
Adaptive Regression for Modeling Nonlinear Relationships
Book 58 of 69: Statistics for Biology and HealthUS$ 131.49
US$ 16.95 shipping
Ships from United Kingdom to U.S.A.Quantity: 2 available
Add to basketHardcover. Condition: Brand New. 400 pages. 9.25x6.25x1.00 inches. In Stock.
-
Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Seller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 4 out of 5 stars
hardcover. Condition: Very Good.
-
Adaptive Regression for Modeling Nonlinear Relationships
Book 58 of 69: Statistics for Biology and HealthSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 145.22
US$ 20.34 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
-
Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Book 58 of 69: Statistics for Biology and HealthUS$ 134.06
US$ 33.90 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketHardcover. Condition: Like New. Like New. book.
-
Condition: As New. Unread book in perfect condition.
-
-
Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 159.62
US$ 16.24 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
-
Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health)
Book 58 of 69: Statistics for Biology and HealthUS$ 148.03
US$ 33.90 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: New. New. book.
-
Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 159.61
US$ 20.34 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New.
-
Condition: As New. Unread book in perfect condition.
-
Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 181.35
US$ 20.34 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
-
-
Condition: Sehr gut. Zustand: Sehr gut | Seiten: 544 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
-
Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Language: English
Published by Springer International Publishing, Springer Nature Switzerland, 2025
ISBN 10: 3031419901 ISBN 13: 9783031419904
Taschenbuch. 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.
-
Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Language: English
Published by Springer International Publishing, Springer Nature Switzerland Jan 2025, 2025
ISBN 10: 3031419901 ISBN 13: 9783031419904
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 544 pp. Englisch.
-
Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Language: English
Published by Springer, Berlin, Springer, 2024
ISBN 10: 3031419871 ISBN 13: 9783031419874
Buch. 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.
-
Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Language: English
Published by Springer Verlag GmbH, 2026
ISBN 10: 303200988X ISBN 13: 9783032009883
US$ 226.13
US$ 57.88 shipping
Ships from Germany to U.S.A.Quantity: Over 20 available
Add to basketCondition: New.
-
Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling
Language: English
Published by Springer, Palgrave Macmillan, 2026
ISBN 10: 303200988X ISBN 13: 9783032009883
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book formulates methods for modeling continuous and categorical correlated outcomes extending 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 uses alternate estimating equations for the mean 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 Hessian matrices, for a multi-step algorithm for solving estimating equations, and model-based and robust empirical tests for assessing theory-based models. Directly specified correlation structures are considered as well as covariance structures based on random effect/coefficients.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 necessarily distinctly 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 as supplementary materials upon request from the author. Detailed descriptions of how to use this macro and interpret its output are provided in the book.
-
US$ 295.72
US$ 20.34 shipping
Ships from United Kingdom to U.S.A.Quantity: 2 available
Add to basketHardcover. Condition: Brand New. 2nd har/psc edition. 565 pages. 9.25x6.10x9.21 inches. In Stock.
-
Condition: new. Questo è un articolo print on demand.
-
Condition: new. Questo è un articolo print on demand.
