9780387400815 - Statistical Tools for Nonlinear Regression: A Practical Guide With S-Plus and R Examples di Huet, Sylvie; Bouvier, Annie; Poursat, Marie-Anne; Jolivet, Emmanuel (19 risultati)

Hardcover. Condizione: Good. This is a used book in good condition and may show some signs of use or wear . This is a used book in good condition and may show some signs of use or wear .

Hardcover. Condizione: Very Good. No Jacket. 2nd Edition. Book is gently used with only minor wear. Spine and bottom corner are bumped. Tightly bound copy with clean interior having no markings, writing, underlining, or highlighting in margins or text block. Inv. # 10968.

Hardcover. Condizione: VG. used hardcover copy in illustrated boards, no jacket, as issued. light shelfwear, corners perhaps slightly bumped. pages and binding are clean, straight and tight. there are no marks to the text or other serious flaws.

Hardcover. Condizione: Very Good. 2nd Edition. Hardcover, xiv + 232 pages, second ed., NOT ex-library. Printed in the USA. Mild internal creasing, else very good. Book is clean and bright with unmarked text and firm binding, free of inscriptions and stamps. Issued without a dust jacket. -- Contents: 1 Nonlinear Regression Model and Parameter Estimation [Examples; Parametric Nonlinear Regression Model; Estimation; Applications (Pasture Regrowth; Cortisol Assay; ELISA Test; Ovocytes; Isomerization); Conclusion and References; Using nls2] 2 Accuracy of Estimators, Confidence Intervals and Tests [Examples; Problem Formulation; Solutions; Applications] 3 Variance Estimation [Examples; Parametric Modeling of the Variance; Estimation (Maximum Likelihood; Quasi-Likelihood; Three-Step Estimation); Tests and Confidence Regions; Applications (Growth of Winter Wheat Tillers; Solubility of Peptides in Trichloacetic Acid Solutions] 4 Diagnostics of Model Misspecification [Problem Formulation; Diagnostics of Model Misspecifications with Graphics; Diagnostics of Model Misspecifications with Tests; Numerical Troubles During the Estimation Process: Peptides Example; Peptides Example: Concluded] 5 Calibration and Prediction [Examples; Problem Formulation; Confidence Intervals; Applications] 6 Binomial Nonlinear Models [Examples (Assay of an Insecticide with a Synergist: A Binomial Nonlinear Model; Vaso-Constriction in the Skin of the Digits: The Case of Binary Response Data; Mortality of Confused Flour Beetles: The Choice of a Link Function in a Binomial Linear Model; Mortality of Confused Flour Beetles 2: Survival Analysis Using a Binomial Nonlinear Model; Germination of Orobranche: Overdispersion); Parametric Binomial Nonlinear Model; Overdispersion, Underdispersion; Estimation; Tests and Confidence Regions; Applications] 7 Multinomial and Poisson Nonlinear Models [Multinomial Model (Pneumoconiosis among Coal Miners: An Example of Multicategory Response Data; A Cheese Tasting Experiment; Parametric Multinomial Model; Estimation in the Multinomial Model; Tests and Confidence Intervals; Pneumoconiosis among Coal Miners: The Multinomial Logit Model; Cheese Tasting Example: Model Based on Cumulative Probabilities; Using nls2); Poisson Model]; References; Index -- Statistical Tools for Nonlinear Regression, Second Edition, presents methods for analyzing data using parametric nonlinear regression models. The new edition has been expanded to include binomial, multinomial and Poisson non-linear models. Using examples from experiments in agronomy and biochemistry, it shows how to apply these methods. It concentrates on presenting the methods in an intuitive way rather than developing the theoretical backgrounds. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Its main advantages are to make the model building, estimation and validation tasks, easy to do. More precisely, Complex models can be easily described using a symbolic syntax. The regression function as well as the variance function can be defined explicitly as functions of independent variables and of unknown parameters or they can be defined as the solution to a system of differential equations. Moreover, constraints on the parameters can easily be added to the model. It is thus possible to test nested hypotheses and to compare several data sets. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap. Some graphical tools are proposed for visualizing the fitted curves, the residuals, the confidence regions, and the numerical estimation procedure.

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Condizione: New. pp. 252 2nd Edition.

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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Statistical Tools for Nonlinear Regression, Second Edition, presents methods for analyzing data using parametric nonlinear regression models. The new edition has been expanded to include binomial, multinomial and Poisson non-linear models. Using examples from experiments in agronomy and biochemistry, it shows how to apply these methods. It concentrates on presenting the methods in an intuitive way rather than developing the theoretical backgrounds. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Its main advantages are to make the model building, estimation and validation tasks, easy to do. More precisely, Complex models can be easily described using a symbolic syntax. The regression function as well as the variance function can be defined explicitly as functions of independent variables and of unknown parameters or they can be defined as the solution to a system of differential equations. Moreover, constraints on the parameters can easily be added to the model. It is thus possible to test nested hypotheses and to compare several data sets. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap. Some graphical tools are proposed for visualizing the fitted curves, the residuals, the confidence regions, and the numerical estimation procedure.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Statistical Tools for Nonlinear Regression presents methods for analyzing data. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap. 256 pp. Englisch.

Condizione: New. PRINT ON DEMAND pp. 252.

Condizione: New. Print on Demand pp. 252 Illus.

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Statistical Tools for Nonlinear Regression presents methods for analyzing data. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models.

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Statistical Tools for Nonlinear Regression, Second Edition, presents methods for analyzing data using parametric nonlinear regression models. The new edition has been expanded to include binomial, multinomial and Poisson non-linear models. Using examples from experiments in agronomy and biochemistry, it shows how to apply these methods. It concentrates on presenting the methods in an intuitive way rather than developing the theoretical backgrounds. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Its main advantages are to make the model building, estimation and validation tasks, easy to do. More precisely, Complex models can be easily described using a symbolic syntax. The regression function as well as the variance function can be defined explicitly as functions of independent variables and of unknown parameters or they can be defined as the solution to a system of differential equations. Moreover, constraints on the parameters can easily be added to the model. It is thus possible to test nested hypotheses and to compare several data sets. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap. Some graphical tools are proposed for visualizing the fitted curves, the residuals, the confidence regions, and the numerical estimation procedure.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 252 pp. Englisch.