Generalized linear models : with applications in engineering and the sciences / / Raymond H. Myers ... [et al.]

• Record Nr.: UNINA9910139730403321
• Pubbl/distr/stampa: Hoboken, N.J., : Wiley, c2010
• ISBN: 9786613446022; 9781283446020; 1283446022; 9780470556986; 0470556986; 9780470556979; 0470556978
• Edizione: [2nd ed.]
• Descrizione fisica: 1 online resource (521 p.)
• Collana: Wiley series in probability and statistics
• Altri autori (Persone): MyersRaymond H
• Disciplina: 519.5/35
• Soggetti: Linear models (Statistics)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Rev. ed. of: Generalized linear models / Raymond H. Myers, Douglas C. Montgomery, G. Geoffrey Vining. c2002.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Generalized Linear Models: With Applications in Engineering and the Sciences; Contents; Preface; 1. Introduction to Generalized Linear Models; 1.1 Linear Models; 1.2 Nonlinear Models; 1.3 The Generalized Linear Model; 2. Linear Regression Models; 2.1 The Linear Regression Model and Its Application; 2.2 Multiple Regression Models; 2.2.1 Parameter Estimation with Ordinary Least Squares; 2.2.2 Properties of the Least Squares Estimator and Estimation of σ2; 2.2.3 Hypothesis Testing in Multiple Regression; 2.2.4 Confidence Intervals in Multiple Regression; 2.2.5 Prediction of New Response Observations2.2.6 Linear Regression Computer Output; 2.3 Parameter Estimation Using Maximum Likelihood; 2.3.1 Parameter Estimation Under the Normal-Theory Assumptions; 2.3.2 Properties of the Maximum Likelihood Estimators; 2.4 Model Adequacy Checking; 2.4.1 Residual Analysis; 2.4.2 Transformation of the Response Variable Using the Box-Cox Method; 2.4.3 Scaling Residuals; 2.4.4 Influence Diagnostics; 2.5 Using R to ; Perform Linear Regression Analysis; 2.6 Parameter Estimation by Weighted Least Squares; 2.6.1 The Constant Variance Assumption; 2.6.2 Generalized and Weighted Least Squares2.6.3 Generalized Least Squares and Maximum Likelihood; 2.7 Designs for Regression Models; Exercises; 3. Nonlinear Regression Models; 3.1 Linear and Nonlinear Regression Models; 3.1.1 Linear Regression Models; 3.1.2 Nonlinear Regression Models; 3.1.3 Origins of Nonlinear Models; 3.2 Transforming to a Linear Model; 3.3 Parameter Estimation in a Nonlinear System; 3.3.1 Nonlinear Least Squares; 3.3.2 The Geometry of Linear and Nonlinear Least Squares; 3.3.3 Maximum Likelihood Estimation; 3.3.4 Linearization and the Gauss-Newton Method; 3.3.5 Using R to Perform Nonlinear Regression Analysis3.3.6 Other Parameter Estimation Methods; 3.3.7 Starting Values; 3.4 Statistical Inference in Nonlinear Regression; 3.5 Weighted Nonlinear Regression; 3.6 Examples of Nonlinear Regression Models; 3.7 Designs for Nonlinear Regression Models; Exercises; 4. Logistic and Poisson Regression Models; 4.1 Regression Models Where the Variance Is a Function of the Mean; 4.2 Logistic Regression Models; 4.2.1 Models with a Binary Response Variable; 4.2.2 Estimating the Parameters in a Logistic Regression Model; 4.2.3 Interpellation of the Parameters in a Logistic Regression Model4.2.4 Statistical Inference on Model Parameters; 4.2.5 Lack-of-Fit Tests in Logistic Regression; 4.2.6 Diagnostic Checking in Logistic Regression; 4.2.7 Classification and the Receiver Operating Characteristic Curve; 4.2.8 A Biological Example of Logistic Regression; 4.2.9 Other Models for Binary Response Data; 4.2.10 More than Two Categorical Outcomes; 4.3 Poisson Regression; 4.4 Overdispersion in Logistic and Poisson Regression; Exercises; 5. The Generalized Linear Model; 5.1 The Exponential Family of Distributions; 5.2 Formal Structure for the Class of Generalized Linear Models
• Sommario/riassunto: Praise for the First Edition ""The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities.""-Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized