Statistical Tools for Nonlinear Regression: A Practical Guide With S-Plus and R Examples - Couverture rigide
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Synopsis
This book provides the user with a quide to data analysis using nonlinear regression models. The book emphasizes examples in biology and agriculture and does not include any proofs. The second edition includes several new topics, such as binominal, multinomial, and Poisson non-linear models.
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Statistical Tools for Nonlinear Regression: A Practical Guide With S-PLUS and R Examples (Springer Series in Statistics)
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Springer, 2003
ISBN 10 : 0387400818
ISBN 13 : 9780387400815
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Statistical Tools for Nonlinear Regression: A Practical Guide with S-Plus and R Examples
Edité par
Springer, 2003
ISBN 10 : 0387400818
ISBN 13 : 9780387400815
Ancien ou d'occasion
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Statistical Tools for Nonlinear Regression: A Practical Guide With S-PLUS and R Examples (Springer Series in Statistics) 2nd Edition
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Springer September 2003, 2003
ISBN 10 : 0387400818
ISBN 13 : 9780387400815
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Statistical Tools for Nonlinear Regression: A Practical Guide with S-PLUS and R Examples
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Springer, New York, NY, 2004
ISBN 10 : 0387400818
ISBN 13 : 9780387400815
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Statistical Tools for Nonlinear Regression: A Practical Guide With S-PLUS and R Examples (Springer Series in Statistics)
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Statistical Tools for Nonlinear Regression: A Practical Guide With S-PLUS and R Examples (Springer Series in Statistics)
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Statistical Tools for Nonlinear Regression : A Practical Guide With S-Plus and R Examples
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Statistical Tools for Nonlinear Regression: A Practical Guide with S-PLUS and R Examples (Springer Series in Statistics)
Edité par
Springer, 2003
ISBN 10 : 0387400818
ISBN 13 : 9780387400815
Ancien ou d'occasion
Couverture rigide
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Hardcover. Etat : 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. N° de réf. du vendeur 005884
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Statistical Tools for Nonlinear Regression : A Practical Guide With S-Plus and R Examples
Edité par
Springer, 2003
ISBN 10 : 0387400818
ISBN 13 : 9780387400815
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 1791971
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Statistical Tools for Nonlinear Regression (Hardcover)
Edité par
Springer-Verlag New York Inc., New York, NY, 2003
ISBN 10 : 0387400818
ISBN 13 : 9780387400815
Neuf
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Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
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Hardcover. Etat : new. Hardcover. 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. This book provides the user with a guide to data analysis using nonlinear regression models. The book emphasizes examples in biology and agriculture and does not include any proofs. The second edition includes several new topics, such as binominal, multinomial, and Poisson non-linear models. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9780387400815
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