Könyv Statistical Tools for Nonlinear Regression Sylvie Huet

Statistical Tools for Nonlinear Regression

A Practical Guide With S-PLUS and R Examples

Nyelv: Angol
Kötés: Kemény kötésű
Elérhetőség: Beszállítói készleten alacsony példányszámban
Küldés 11-15 napon belül
20 440 Ft
Statistical Tools for Nonlinear Regression, Second Edition, presents methods for analyzing data usin...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2003
oldal
234
EAN
9780387400815
ISBN
0387400818
Enbook ID
01382114
Súly
500
Méretek
152 x 234 x 18

Teljes leírás

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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