Applied Bayesian hierarchical methods. (2010)

• Record Type: Book
• Further Information: Note: Peter D. Congdon.
• Other Names: Congdon, P
• Contents: Bayesian Methods for Complex Data: Estimation and Inference ; Introduction; Posterior Inference from Bayes Formula; Markov Chain Sampling in Relation to Monte Carlo Methods: Obtaining Posterior Inferences; Hierarchical Bayes Applications; Metropolis Sampling; Choice of Proposal Density; Obtaining Full Conditional Densities; Metropolis–Hastings Sampling; Gibbs Sampling; Assessing Efficiency and Convergence: Ways of Improving Convergence; Choice of Prior Density Model Fit, Comparison, and Checking; Introduction; Formal Methods: Approximating Marginal Likelihoods; Effective Model Dimension and Deviance Information Criterion; Variance Component Choice and Model Averaging; Predictive Methods for Model Choice and Checking; Estimating Posterior Model Probabilities Hierarchical Estimation for Exchangeable Units: Continuous and Discrete Mixture Approaches ; Introduction; Hierarchical Priors for Ensemble Estimation using Continuous Mixtures; The Normal-Normal Hierarchical Model and Its Applications; Priors for Second Stage Variance Parameters; Multivariate Meta-Analysis; Heterogeneity in Count Data: Hierarchical Poisson Models; Binomial and Multinomial Heterogeneity; Discrete Mixtures and Nonparametric Smoothing Methods; Nonparametric Mixing via Dirichlet Process and Polya Tree Priors Structured Priors Recognizing Similarity over Time and Space ; Introduction; Modeling Temporal Structure: Autoregressive Models; State Space Priors for Metric Data; Time Series for Discrete Responses: State Space Priors and Alternatives; Stochastic Variances; Modeling Discontinuities in Time; Spatial Smoothing and Prediction for Area Data; Conditional Autoregressive Priors; Priors on Variances in Conditional Spatial Models; Spatial Discontinuity and Robust Smoothing; Models for Point Processes Regression Techniques using Hierarchical Priors ; Introduction; Regression for Overdispersed Discrete Data; Latent Scales for Binary and Categorical Data; Nonconstant Regression Relationships and Variance Heterogeneity; Heterogeneous Regression and Discrete Mixture Regressions; Time Series Regression: Correlated Errors and Time-Varying Regression Effects; Spatial Correlation in Regression Residuals; Spatially Varying Regression Effects: Geographically Weighted Linear Regression and Bayesian Spatially Varying Coefficient Models Bayesian Multilevel Models; Introduction; The Normal Linear Mixed Model for Hierarchical Data; Discrete Responses: General Linear Mixed Model, Conjugate, and Augmented Data Models; Crossed and Multiple Membership Random Effects; Robust Multilevel Models Multivariate Priors, with a Focus on Factor and Structural Equation Models; Introduction; The Normal Linear SEM and Factor Models; Identifiability and Priors on Loadings; Multivariate Exponential Family Outcomes and General Linear Factor Models; Robust Options in Multivariate and Factor Analysis; Multivariate Spatial Priors for Discrete Area Frameworks; Spatial Factor Models; Multivariate Time Series Hierarchical Models for Panel Data ; Introduction; General Linear Mixed Models for Panel Data; Temporal Correlation and Autocorrelated Residuals; Categorical Choice Panel Data; Observation-Driven Autocorrelation: Dynamic Panel Models; Robust Panel Models: Heteroscedasticity, Generalized Error Densities, and Discrete Mixtures; Multilevel, Multivariate, and Multiple Time Scale Longitudinal Data; Missing Data in Panel Models Survival and Event History Models ; Introduction; Survival Analysis in Continuous Time; Semiparametric Hazards; Including Frailty; Discrete Time Hazard Models; Dependent Survival Times: Multivariate and Nested Survival Times; Competing Risks Hierarchical Methods for Nonlinear Regression ; Introduction; Nonparametric Basis Function Models for the Regression Mean; Multivariate Basis Function Regression; Heteroscedasticity via Adaptive Nonparametric Regression; General Additive Methods; Nonparametric Regression Methods for Longitudinal Analysis Appendix: Using WinBUGS and BayesX References Index
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2010
• Extent: 1 online resource (xiii, 590 pages)
• Subjects: 519.5/42; Multilevel models (Statistics); Bayesian statistical decision theory; Bayesian statistical decision theory; Multilevel models (Statistics); Electronic books
• Languages: English
• ISBNs: 9781584887218; 1584887214
• Related ISBNs: 9781584887201
• Notes: Note: Includes bibliographical references and index.; Note: Print version record.
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