TY - BOOK ID - 16144162 TI - Statistical Tools for Nonlinear Regression : A Practical Guide With S-PLUS and R Examples AU - Huet, Sylvie. AU - Bouvier, Anne. AU - Poursat, Marie-Anne. AU - Jolivet, Emmanuel. PY - 2004 SN - 0387400818 9786610189113 1280189118 0387215743 PB - New York, NY : Springer New York : Imprint: Springer, DB - UniCat KW - Regression analysis. KW - Nonlinear theories. KW - Parameter estimation. KW - 57.087.1 KW - 519.233.5 KW - #SBIB:303H520 KW - #SBIB:303H61 KW - 519.233.5 Correlation analysis. Regression analysis KW - Correlation analysis. Regression analysis KW - 57.087.1 Biometry. Statistical study and treatment of biological data KW - Biometry. Statistical study and treatment of biological data KW - Methoden sociale wetenschappen: techniek van de analyse, algemeen KW - Wiskundige methoden en technieken KW - Statistics. KW - Statistical Theory and Methods. KW - Statistics for Life Sciences, Medicine, Health Sciences. KW - Nonlinear theories KW - Parameter estimation KW - Regression analysis KW - Analysis, Regression KW - Linear regression KW - Regression modeling KW - Multivariate analysis KW - Structural equation modeling KW - Estimation theory KW - Stochastic systems KW - Nonlinear problems KW - Nonlinearity (Mathematics) KW - Calculus KW - Mathematical analysis KW - Mathematical physics KW - Mathematical statistics. KW - Statistics . KW - Statistical analysis KW - Statistical data KW - Statistical methods KW - Statistical science KW - Mathematics KW - Econometrics UR - https://www.unicat.be/uniCat?func=search&query=sysid:16144162 AB - 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 is aimed at scientists who are not familiar with statistical theory, but have a basic knowledge of statistical concepts. It includes methods based on classical nonlinear regression theory and more modern methods, such as bootstrap, which have proved effective in practice. The additional chapters of the second edition assume some practical experience in data analysis using generalized linear models. The book will be of interest both for practitioners as a guide and a reference book, and for students, as a tutorial book. Sylvie Huet and Emmanuel Jolivet are senior researchers and Annie Bouvier is computing engineer at INRA, National Institute of Agronomical Research, France; Marie-Anne Poursat is associate professor of statistics at the University Paris XI. ER -