9783319339443 - adaptive regression for modeling nonlinear relationships (statistics for biology and health) de knafl, george j.; ding, kai (16 resultados)

Idioma: Inglés
Editorial: Springer, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
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Librería: Zubal-Books, Since 1961, Cleveland, OH, Estados Unidos de AmericaZubal-Books, Since 1961
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Idioma: Inglés
Editorial: Springer, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
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Idioma: Inglés
Editorial: Springer, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
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Idioma: Inglés
Editorial: Springer, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
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Idioma: Inglés
Editorial: Springer, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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Condición: New. pp. 280.

Idioma: Inglés
Editorial: Springer Verlag, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Hardcover. Condición: Brand New. 400 pages. 9.25x6.25x1.00 inches. In Stock.

Idioma: Inglés
Editorial: Springer International Publishing, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Buch. Condición: 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 i…nto 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.

Idioma: Inglés
Editorial: Springer, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
- Tapa dura
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Idioma: Inglés
Editorial: Springer, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
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Idioma: Inglés
Editorial: Springer, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
- Tapa dura
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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Idioma: Inglés
Editorial: Springer International Publishing Sep 2016, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condición: 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 predi…ctor 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.

Idioma: Inglés
Editorial: Springer, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
- Tapa dura
- Impresión bajo demanda
Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condición: new. Questo è un articolo print on demand.

Idioma: Inglés
Editorial: Springer International Publishing, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
- Tapa dura
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Librería: moluna, Greven, Alemaniamoluna
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Condición: 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 .

Idioma: Inglés
Editorial: Springer, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
- Tapa dura
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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Condición: New. Print on Demand pp. 280.

Idioma: Inglés
Editorial: Springer, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
- Tapa dura
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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Condición: New. PRINT ON DEMAND pp. 280.

Idioma: Inglés
Editorial: Springer, Palgrave Macmillan Sep 2016, 2016
Serie: Statistics for Biology and Health, Libro 58 de 69. Libro 58 de 69 - Statistics for Biology and Health
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 400 pp. Englisch.