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Mixed Models : Theory and Applications with R.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Demidenko, Eugene
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Wiley, 2013.
Colección:Wiley series in probability and statistics ; 893.
Temas:
Acceso en línea:Texto completo

MARC

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490 1 |a Wiley series in probability and statistics ;  |v 893 
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504 |a Includes bibliographical references and index. 
505 0 |a Cover -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Preface to the Second Edition -- R Software and Functions -- Data Sets -- Open Problems in Mixed Models -- 1 Introduction: Why Mixed Models? -- 1.1 Mixed effects for clustered data -- 1.2 ANOVA, variance components, and the mixed model -- 1.3 Other special cases of the mixed effects model -- 1.4 Compromise between Bayesian and frequentist approaches -- 1.5 Penalized likelihood and mixed effects -- 1.6 Healthy Akaike information criterion -- 1.7 Penalized smoothing -- 1.8 Penalized polynomial fitting 
505 8 |a 1.9 Restraining parameters, or what to eat -- 1.10 Ill-posed problems, Tikhonov regularization, and mixed effects -- 1.11 Computerized tomography and linear image reconstruction -- 1.12 GLMM for PET -- 1.13 Maple leaf shape analysis -- 1.14 DNA Western blot analysis -- 1.15 Where does the wind blow? -- 1.16 Software and books -- 1.17 Summary points -- 2 MLE for the LME Model -- 2.1 Example: weight versus height -- 2.1.1 The first R script -- 2.2 The model and log-likelihood functions -- 2.2.1 The model -- 2.2.2 Log-likelihood functions -- 2.2.3 Dimension-reduction formulas 
505 8 |a 2.2.4 Profile log-likelihood functions -- 2.2.5 Dimension-reduction GLS estimate -- 2.2.6 Restricted maximum likelihood -- 2.2.7 Weight versus height (continued) -- 2.3 Balanced random-coefficient model -- 2.4 LME model with random intercepts -- 2.4.1 Balanced random-intercept model -- 2.4.2 How random effect affects the variance of MLE -- 2.5 Criterion for MLE existence -- 2.6 Criterion for the positive definiteness of matrix D -- 2.6.1 Example of an invalid LME model -- 2.7 Pre-estimation bounds for variance parameters -- 2.8 Maximization algorithms 
505 8 |a 2.9 Derivatives of the log-likelihood function -- 2.10 Newton-Raphson algorithm -- 2.11 Fisher scoring algorithm -- 2.11.1 Simplified FS algorithm -- 2.11.2 Empirical FS algorithm -- 2.11.3 Variance-profile FS algorithm -- 2.12 EM algorithm -- 2.12.1 Fixed-point algorithm -- 2.13 Starting point -- 2.13.1 FS starting point -- 2.13.2 FP starting point -- 2.14 Algorithms for restricted MLE -- 2.14.1 Fisher scoring algorithm -- 2.14.2 EM algorithm -- 2.15 Optimization on nonnegative definite matrices -- 2.15.1 How often can one hit the boundary? -- 2.15.2 Allow matrix D to be not nonnegative definite 
505 8 |a 2.15.3 Force matrix D to stay nonnegative definite -- 2.15.4 Matrix D reparameterization -- 2.15.5 Criteria for convergence -- 2.16 lmeFS and lme in R -- 2.17 Appendix: proof of the existence of MLE -- 2.18 Summary points -- 3 Statistical Properties of the LME Model -- 3.1 Introduction -- 3.2 Identifiability of the LME model -- 3.2.1 Linear regression with random coefficients -- 3.3 Information matrix for variance parameters -- 3.3.1 Efficiency of variance parameters for balanced data -- 3.4 Profile-likelihood confidence intervals -- 3.5 Statistical testing of the presence of random effects 
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830 0 |a Wiley series in probability and statistics ;  |v 893. 
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