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Robust mixed model analysis

Author: Jiming Jiang
Publisher: New Jersey : World Scientific, 2019.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:

Mixed-effects models have found broad applications in various fields. As a result, the interest in learning and using these models is rapidly growing.

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Genre/Form: Electronic books
Problems and exercises
Problems, exercises, etc
Additional Physical Format: Print version:
(OCoLC)933265578
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jiming Jiang
ISBN: 9789814733847 9814733849
OCLC Number: 1100429434
Description: 1 online resource
Contents: Intro; Contents; Preface; 1. Introduction; 1.1 Illustrative example; 1.2 Outline of approaches to robust mixed model analysis; 1.3 A roadmap; 1.4 Exercises; 2. Generalized Estimating Equations; 2.1 Method of moments; 2.2 Generalized estimating equations (GEE); 2.3 Iterative estimating equations; 2.4 Robust estimation in GLMM; 2.5 Robust GEE; 2.6 Real-data examples; 2.6.1 Hip replacement data revisited; 2.6.2 Salamander-mating data; 2.6.3 Epileptic seizure data; 2.7 Exercises; 3. Non-Gaussian Linear Mixed Models; 3.1 Types of models; 3.2 Quasi-likelihood method; 3.3 Measure of uncertainty 3.3.1 Empirical method of moments3.3.2 Partially observed information; 3.3.3 Hypothesis testing: A simulated example; 3.3.4 Consistency of POI; 3.4 Real-data example; 3.5 Exercises; 4. Robust Tests; 4.1 Robust dispersion tests; 4.2 Robust versions of classical tests; 4.2.1 Basic idea, assumptions, and examples; 4.2.2 The W-, S-, and L-test statistics; 4.2.3 Asymptotic theory; 4.3 Robust classical tests for mixed ANOVA model; 4.4 A unified robust goodness-of-fit test; 4.4.1 Tailoring; 4.4.2 Application to SAE; 4.4.3 Empirical results; 4.5 Real-data examples; 4.5.1 TVSFP data 4.5.2 Median income data4.6 Exercises; 5. Observed Best Prediction; 5.1 Best linear unbiased prediction; 5.2 Observed best prediction: Another look at the BLUP; 5.3 Example; 5.4 OBP for two classes of SAE models; 5.4.1 Fay-Herriot model; 5.4.2 Nested-error regression model; 5.5 OBP for small area counts; 5.5.1 Best prediction under a two-stage model; 5.5.2 Derivation of OBP; 5.5.3 Extensions; 5.6 Asymptotic property of BPE; 5.7 Examples with simulations; 5.7.1 A simple example; 5.7.2 Cases when the assumed model is correct, or partially correct 5.7.3 Comparison of OBP and EBP under an LN model5.8 Estimation of area-specific MSPE; 5.9 Real-data examples; 5.9.1 Hospital data; 5.9.2 TVSFP data revisited; 5.9.3 Minnesota county data; 5.10 Exercises; 6. Model Selection; 6.1 Generalized information criteria; 6.1.1 Selecting the fixed covariates only; 6.1.2 Selecting fixed covariates and random effect factors; 6.1.3 A robust approximation to BIC; 6.1.4 A robust conditional AIC for LMM; 6.2 The fence methods; 6.2.1 Adaptive fence; 6.2.2 Invisible fence; 6.2.3 Model selection with incomplete data; 6.2.4 Examples 6.3 Shrinkage mixed model selection6.3.1 An E-M based approach; 6.3.2 Predictive shrinkage selection; 6.3.3 Real-data example: Analysis of high-speed network; 6.4 Exercises; 7. Other Topics; 7.1 Mixed model diagnostics; 7.2 Nonparametric/semiparametric methods; 7.2.1 A P-spline nonparametric model; 7.2.2 Nonparametric model selection; 7.2.3 Examples; 7.2.4 Functional and semiparametric mixed models; 7.2.5 Nonparametric bootstrapping; 7.3 Bayesian analysis; 7.3.1 A robust hierarchical Bayes method; 7.3.2 Real-data example: Iowa crops data; 7.3.3 Bayesian empirical likelihood
Responsibility: by Jiming Jiang (University of California, Davis, USA).

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