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Generalized linear models : with applications in engineering and the sciences / Raymond H. Myers, Douglas C. Montgomery, G. Geoffrey Vining.

Por: Colaborador(es): Tipo de material: TextoTextoSeries Wiley series in probability and statisticsEditor: New York : J. Wiley, [2002]Fecha de copyright: ©2002Descripción: xiii, 342 páginas : ilustraciones ; 24 cmTipo de contenido:
  • texto
Tipo de medio:
  • sin mediación
Tipo de soporte:
  • volumen
Tema(s): Clasificación LoC:
  • QA 276 M94.2002
Contenidos:
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. 2.3. Parameter Estimation Using Maximum Likelihood. 2.4. Model Adequacy Checking. 2.5. Parameter Estimation by Weighted Least Squares -- 3. Nonlinear Regression Models. 3.1. Linear and Nonlinear Regression Models. 3.2. Transforming to a Linear Model. 3.3. Parameter Estimation in a Nonlinear System. 3.4. Statistical Inference in Nonlinear Regression. 3.5. Weighted Nonlinear Regression. 3.6. Examples of Nonlinear Regression Models -- 4. Logistic and Poisson Regression Models. 4.1. Regression Models Where the Variance Is a Function of the Mean. 4.2. The Logistic Regression Model. 4.3. Parameter Estimation Using Maximum Likelihood. 4.4. Different Forms of Statistical Inference Using Logistic Regression. 4.5. Examples Using Logistic Regression. 4.6. Other Considerations in Logistic Regression. 4.7. The Concept of Overdispersion in Logistic Regression. 4.8. Introduction to Poisson Regression. 4.9. Maximum Likelihood Estimators for Poisson Regression. 4.10. Applications in Poisson Regression. 4.11. Examples Using Poisson Regression. 4.12. Classification Variables and Extensions to the Anova Model -- 5. The Family of Generalized Linear Models. 5.1. The Exponential Family of Distributions. 5.2. Formal Structure for the Class of Generalized Linear Models. 5.3. Likelihood Equations for Generalized Linear Models. 5.4. Quasi-likelihood. 5.5. Other Important Distributions for Generalized Linear Models. 5.6. A Class of Link Functions -- The Power Function. 5.7. Inference and Residual Analysis for Generalized Linear Models. 5.8. Examples with the Gamma Distribution -- 6. Generalized Estimating Equations. 6.1. Data Layout for Longitudinal Studies. 6.2. Impact of the Correlation Matrix R. 6.3. Iterative Procedure in the Normal Case,
Identi Link. 6.4. Generalized Estimating Equations for More Generalized Linear Models -- 7. Further Advances and Applications in GLM. 7.1. Introduction. 7.2. Experimental Designs for Generalized Linear Models. 7.3. Quality of Asymptotic Results and Related Issues. 7.4. GLM Analysis of Screening Experiments. 7.5. GLM and Data Transformation. 7.6. Modeling Both a Process Mean and Process Variance Using GLM. 7.7. Generalized Additive Models -- App. A.1. Background on Basic Test Statistics -- App. A.2. Background from the Theory of Linear Models -- App. A.3. The Gauss-Markov Theorem, var([epsilon]) = [sigma][superscript 2]I -- App. A.4. The Relationship Between Maximum Likelihood Estimation of the Logistic Regression Model and Weighted Least Squares -- App. A.5. Computational Details for GLMs for a Canonical Link -- App. A.6. Computational Details for GLMs for a Noncanonical Link.
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Libros Biblioteca Francisco Xavier Clavigero Acervo Acervo General QA 276 M94.2002 (Navegar estantería(Abre debajo)) ej. 1 Disponible UIA175045

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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. 2.3. Parameter Estimation Using Maximum Likelihood. 2.4. Model Adequacy Checking. 2.5. Parameter Estimation by Weighted Least Squares -- 3. Nonlinear Regression Models. 3.1. Linear and Nonlinear Regression Models. 3.2. Transforming to a Linear Model. 3.3. Parameter Estimation in a Nonlinear System. 3.4. Statistical Inference in Nonlinear Regression. 3.5. Weighted Nonlinear Regression. 3.6. Examples of Nonlinear Regression Models -- 4. Logistic and Poisson Regression Models. 4.1. Regression Models Where the Variance Is a Function of the Mean. 4.2. The Logistic Regression Model. 4.3. Parameter Estimation Using Maximum Likelihood. 4.4. Different Forms of Statistical Inference Using Logistic Regression. 4.5. Examples Using Logistic Regression. 4.6. Other Considerations in Logistic Regression. 4.7. The Concept of Overdispersion in Logistic Regression. 4.8. Introduction to Poisson Regression. 4.9. Maximum Likelihood Estimators for Poisson Regression. 4.10. Applications in Poisson Regression. 4.11. Examples Using Poisson Regression. 4.12. Classification Variables and Extensions to the Anova Model -- 5. The Family of Generalized Linear Models. 5.1. The Exponential Family of Distributions. 5.2. Formal Structure for the Class of Generalized Linear Models. 5.3. Likelihood Equations for Generalized Linear Models. 5.4. Quasi-likelihood. 5.5. Other Important Distributions for Generalized Linear Models. 5.6. A Class of Link Functions -- The Power Function. 5.7. Inference and Residual Analysis for Generalized Linear Models. 5.8. Examples with the Gamma Distribution -- 6. Generalized Estimating Equations. 6.1. Data Layout for Longitudinal Studies. 6.2. Impact of the Correlation Matrix R. 6.3. Iterative Procedure in the Normal Case,

Identi Link. 6.4. Generalized Estimating Equations for More Generalized Linear Models -- 7. Further Advances and Applications in GLM. 7.1. Introduction. 7.2. Experimental Designs for Generalized Linear Models. 7.3. Quality of Asymptotic Results and Related Issues. 7.4. GLM Analysis of Screening Experiments. 7.5. GLM and Data Transformation. 7.6. Modeling Both a Process Mean and Process Variance Using GLM. 7.7. Generalized Additive Models -- App. A.1. Background on Basic Test Statistics -- App. A.2. Background from the Theory of Linear Models -- App. A.3. The Gauss-Markov Theorem, var([epsilon]) = [sigma][superscript 2]I -- App. A.4. The Relationship Between Maximum Likelihood Estimation of the Logistic Regression Model and Weighted Least Squares -- App. A.5. Computational Details for GLMs for a Canonical Link -- App. A.6. Computational Details for GLMs for a Noncanonical Link.