Applied Bayesian Modelling - Free

viiiCONTENTS7.4 Direct modelling of spatial covariation in regressionand interpolation applications 2897.4.1 Covariance modelling in regression 2907.4.2 Spatial interpolation 2917.4.3 Variogram methods 2927.4.4 Conditional specification of spatial error 2937.5 Spatial heterogeneity: spatial expansion, geographicallyweighted regression, and multivariate errors 2987.5.1 Spatial expansion model 2987.5.2 Geographicallyweighted regression 2997.5.3 Varying regressions effects via multivariate priors 3007.6 Clustering in relation to known centres 3037.6.1 Areas vs. case events as data 3067.6.2 Multiple sources 3067.7 Spatio-temporal models 3107.7.1 Space-time interaction effects 3127.7.2 Area Level Trends 3127.7.3 Predictor effects in spatio-temporal models 3137.7.4 Diffusion processes 3147.8 Review 316References 317Exercises 320Chapter 8 Structural Equation and Latent Variable Models 3238.1 Introduction 3238.1.1 Extensions to other applications 3258.1.2 Benefits of Bayesian approach 3268.2 Confirmatoryfactor analysis with a single group 3278.3 Latent trait and latent class analysis for discrete outcomes 3348.3.1 Latent class models 3358.4 Latent variables in panel and clustered data analysis 3408.4.1 Latent trait models for continuous data 3418.4.2 Latent class models through time 3418.4.3 Latent trait models for time varying discrete outcomes 3438.4.4 Latent trait models for clustered metric data 3438.4.5 Latent trait models for mixed outcomes 3448.5 Latent structure analysis for missing data 3528.6 Review 357References 358Exercises 360Chapter 9 Survival and Event History Models 3619.1 Introduction 3619.2 Continuous time functions for survival 3639.3 Accelerated hazards 3709.4 Discrete time approximations 3729.4.1 Discrete time hazards regression 375