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Generalized linear models : with applications in engineering and the sciences.
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
John Wiley & Sons,
2012.
|
| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Myers, Raymond H. | |
| 245 | 1 | 0 | |a Generalized linear models : |b with applications in engineering and the sciences. |
| 250 | |a 2nd ed. | ||
| 260 | |a Hoboken : |b John Wiley & Sons, |c 2012. | ||
| 300 | |a 1 online resource (521 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 505 | 0 | |a 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 s2; 2.2.3 Hypothesis Testing in Multiple Regression; 2.2.4 Confidence Intervals in Multiple Regression. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 500 | |a 5.2 Formal Structure for the Class of Generalized Linear Models. | ||
| 520 | |a 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."--TechnometricsGeneralized 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 linear models (GLMs). Main. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Linear models (Statistics) | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x Multivariate Analysis. |2 bisacsh | |
| 650 | 7 | |a Linear models (Statistics) |2 fast | |
| 700 | 1 | |a Montgomery, Douglas C. | |
| 700 | 1 | |a Vining, G. Geoffrey, |d 1954- |1 https://id.oclc.org/worldcat/entity/E39PBJwtjd3cGm7mWR9JD8VQv3 | |
| 700 | 1 | |a Robinson, Timothy J. | |
| 758 | |i has work: |a Generalized linear models (Text) |1 https://id.oclc.org/worldcat/entity/E39PCG6Hf6gr3GMhhTPKDBXtyq |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Myers, Raymond H. |t Generalized Linear Models : with Applications in Engineering and the Sciences. |d Hoboken : John Wiley & Sons, ©2012 |z 9780470454633 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=698819 |z Texto completo |
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