Statistical Tools For Nonlinear Regression: A Practical Guide With S-PLUS and R Examples (Second Edition)

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. Nonlinear Regression Model And Parameter Estimation -- Accuracy Of Estimators, Confidence Intervals And Tests -- Variance Estimation -- Diagnostics Of Model Misspecification -- Calibration And Prediction -- Binomial Non-linear Models -- Multinomial And Poisson Non-linear Models. By S. Huet, A. Bouvier, M. -a. Poursat, E. Jolivet. This book presents the contemporary statistical methods and theory of nonlinear time series analysis. The principal focus is on nonparametric and semiparametric techniques developed in the last decade. It covers the techniques for modeling in state-space, in frequency-domain, as well as in time-domain. To reflect the integration of parametric and nonparametric methods in analyzing time series data, the book also presents an up-to-date exposure of some parametric nonlinear models, including ARCH/GARCH models and threshold models. A compact view on linear ARMA models is also provided. Data arising in real applications are used throughout to show how nonparametric approaches may help to reveal local structure in high-dimensional data. Important technical tools are also introduced.The book will be useful for graduate students, application-oriented time series analysts, and new and experienced researchers. It will have value both within the statistical community and across a broad spectrum of other fields such as econometrics, empirical finance, population biology, and ecology. The prerequisties are basic courses in probability and statistics. This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. A distinct feature of this book is that it applies many modern nonparametric estimation and testing ideas to time series modeling and model identification, while outlines many useful ideas from more traditional time series analysis. This will enable readers to use modern data-analytic techniques while keeping in touch with traditional approaches, and make the book self-contained. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics. Statistical Tools for Nonlinear Regression presents methods for analyzing data using parametric nonlinear regression models. Using examples from experiments in agronomy and biochemistry, it shows how to apply the methods. Aimed at scientists who are not familiar with statistical theory, it concentrates on presenting the methods in an intuitive way rather than developing the theoretical grounds. The book includes methods based on classical nonlinear regression theory and more modern methods, such as the bootstrap, that have proven effective in practice. The examples are analyzed with the software nls2 implemented in S-PLUS. This book presents methods for analyzing data using parametric nonlinear regression models. Using examples from experiments in agronomy and biochemistry, it shows how to apply the methods. Aimed at scientists who are not familiar with statistical theory, it concentrates on presenting the methods in an intuitive way rather than developing the theoretical grounds. The book includes methods based on classical nonlinear regression theory and more modern methods such as the bootstrap that have proven effective in practice. The examples are analyzed with the software program nls2 implemented in S-PLUS. "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 to practitioners as a guide and reference book, and to students as a tutorial book."--Jacket This book provides the user with a guide to data analysis using non-linear 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