|
|
 |
| Àâòîðèçàöèÿ |
|
|
|
| Ïîèñê ïî óêàçàòåëÿì |
|
|
|
|
|
|
|
 |
|
|
|
| Ghosh Sujit K., Mallick Bani K., Dey Dipak K. — Generalized Linear Models: A Bayesian Perspective |
|
|
|
| Ïðåäìåòíûé óêàçàòåëü |
Ability parameters, conditional distribution, item response modeling 184
Absolute residuals, male flour beetle data set 268t
Acute myocardial infarction practice, guidelinespost see “Post acute myocardial infarction practice guidelines”
Additive Log Ratio (ALR), Aitchison’s transformation 359—361
Additive Log Ratio (ALR), function 349—350
Adjusted density method (ADM), small area inference 95—96
AIDS clinical trials, variable selection, predictive approach 288
Aitchison’s Additive Log Ratio (ALR) transformation 359—361
Akaike Information Criteria (AIC) 16
Albert’s model 7
Alcohol consumption, errors-in-variables 340
Analysis of regression models, censored survival data 287
Analysis of survival data mixture-model approach 255—268
Analysis of survival data mixture-model approach, Gibbs sampler 259—260
Analysis of survival data mixture-model approach, MCEM 256—259
Analysis of survival data mixture-model approach, mixture-model approach example 261—268
Analysis of survival data mixture-model approach, mixture-model approach example, EM algorithm 262—263
Analysis of survival data mixture-model approach, mixture-model approach example, Gibbs samplers 263—265
Analysis of survival data mixture-model approach, mixture-model approach example, numerical results 265—268
Analysis of survival data mixture-model approach, model selection 260—261
Analysis of survival models, MCMC 287
Asymmetric links, generation 239
Asymptotic theory, MLE 5
Autocorrelated random effects 30
Autoregressive (AR) model of Ord 27
Baseline hazard rate, model 289
Baseline hazard rate, model, cumulative 289—290
Baseline hazard rate, prior distribution, Cox variable selection 289—292
Basic marginal likelihood identity 124
Basis functions, classification trees 366—367
Basis functions, classification trees, example 366f
Bayesian analysis, classification trees 368—369
Bayesian analysis, compositional data 349—362
Bayesian analysis, compositional data, parametric approach 351—352
Bayesian analysis, compositional data, posterior distributions and estimation 355—359
Bayesian analysis, compositional data, results 359—361 360t—361t
Bayesian analysis, compositional data, semiparametric approach 354—355
Bayesian analysis, compositional data, simulation based model, determination 352—354
Bayesian analysis, correlated ordinal data models 133—155
Bayesian analysis, GLM 273—284
Bayesian analysis, informative prior elicitation 44—46
Bayesian analysis, likelihood analysis 6
Bayesian analysis, logit regression model 243
Bayesian analysis, model choice 15—16
Bayesian computations 36—37
Bayesian deviance 16
Bayesian fitting, one-parameter model, item response modeling 185
Bayesian generalized linear models, post AMI practice guideline development 209—210
Bayesian generalized linear models, small area inference 89—105
Bayesian generalized linear models, small area inference, challenges and future directions 102—104
Bayesian generalized linear models, small area inference, computational issues 96—100
Bayesian generalized linear models, small area inference, Poisson regression models 94—96
Bayesian generalized linear models, small area inference, U.S. mortality data 100—102
Bayesian graphical models, computation 388—389
Bayesian graphical models, conditional independence structures 387—389
Bayesian graphical models, constructing software 389
Bayesian graphical models, marginal posterior distribution 388
Bayesian graphical models, Markov chain Monte Carlo, (MCMC) methods 388—389
Bayesian graphical models, WinBUGS 389
Bayesian hierarchical logistic regression, coronary angiography appropriateness 202—203
Bayesian hierarchical logistic regression, post AMI practice guideline development 209—210
Bayesian inferences, hierarchical GLMMs 36—37
Bayesian inferences, Markov Chain Monte Carlo, (MCMC)-based approaches 62—65
Bayesian MARS (BMARS) 222—223
Bayesian MARS (BMARS), generalized linear models 221—228
Bayesian MARS (BMARS), generalized linear models, motivating example 224—225
Bayesian MARS (BMARS), generalized linear models, Pima Indian example 225—228
Bayesian method, correlated binary data 113—129
Bayesian method, time series count data 159—171
Bayesian model 5—8
Bayesian model, adequacy criterion 14—15
Bayesian model, based method, post AMI practice guideline development 198—199
Bayesian model, diagnostics, correlated binary data 313—326
Bayesian model, multivariate exponential power distribution family links (MVEP) 137
Bayesian model, ordinal probit, post AMI practice guideline development 209—210
Bayesian model, partition, classification trees 371
Bayesian procedure, Markov Chain Monte Carlo (MCMC) implementation 12
Bayesian residual posterior distribution, box plots 250 250f
Bayesian residuals, item response modeling 186—187
Bayesian two-stage prior distribution, small area inference 96—97
Bayesian variable selection 48—50
Bayesian variable selection, Cox model 287—309
Bayesian variable selection, Gibbs sampler 273—284
Bayesian view, generalized linear models (GLMS) 3—17
BAYESTAT 121 122—123
BAYESTAT, correlated binary data 127
Bayes’ approach, linear 60—61
Bayes’ Theorem 59
Berkson measurement error model 332 341
Bernoulli distribution 390
Bernoulli random variables, small area inference 91—92
Beta processes 10
Binary regression, data adaptive Bayesian analysis 244—248
Binary regression, data adaptive Bayesian analysis, exponential power distribution 246—248
Binary regression, data adaptive Bayesian analysis, normal distribution 245
Binary regression, data adaptive robust link functions 243—251
Binary regression, data adaptive robust link functions, binary regression model 244—248
Binary regression, data adaptive robust link functions, numerical illustration 248—250
Binary regression, data adaptive robust link functions, outliers detection 248
Binary regression, nonparametric approach 219
Binary regression, parametric family of link functions 232
Binary response hierarchical model, correlated binary data 122
Binary response regression, application 237—239
Binary response regression, Dirichlet process prior 234—236
Binary response regression, finite mixture model 233—234
Binary response regression, general mixtures 234—236
Binary response regression, link function g modeling 218—219
Binary response regression, model diagnostic 236—237
Binary response regression, normal scale mixture links 231—240
Binomial distribution 5 25 390
Binomial logit hierarchical model 8
Binomial logit hierarchical model, small area inference 97
Birth step, classification trees 368
Box-Cox transformation 350—351 359—361
Boxplots, Bayesian residual posterior distribution 250 251f
Boxplots, kyphosis dataset 226f
Boxplots, posterior, multivariate probit (MVP) 119f
Boxplots, posterior, Probit hierarchical model 123f
Boxplots, posterior, probit normal model 121f
Breast cancer, classification trees 369—371 370f 371t
Brooks’s method 8
Brooks’s method, small area inference 97
Bugs 16 (see also “WinBUGS”)
BUGS, codes 282—284)
BUGS, codes, log-linear models for  contingency table 282—283
BUGS, codes, logistic models with 2 binary explanatory factors 283—284
BUGS, small area inference 94
BUGS, small area inference, Markov Chain Monte Carlo (MCMC) 96
Can Evaluate method, graphical model 403
Cancer clinical trials, variable selection, predictive approach 288
Canonical link function 224
Canonical link function, small area inference 98
Canonical parameters 5 12 13
Cargo data analysis, OGLMs 80
Carlin and Chib’s algorithm, conditional distribution sampling 122 129
Carlin and Chib’s method 275
Carlin and Chib’s method, contingency table 278—281
Carlin and Chib’s method, example 278—281
Carlin and Chib’s method, Gibbs sampler variable selection strategies 275
Carlin and Chib’s method, log-linear model example 280 280t
Carlin and Chib’s method, logistic regression model, example 280—281 281t
Carlin and Chib’s method, Stochastic Search Variable Selection (SSVS) 276
Carlin and Chib’s method, unconditional priors 277
Carstairs’ index 333
Categorical data, GLM 365
Censored survival data analysis of regression models 287
Check method, graphical model 403
Chen and Shao method, Monte Carlos posterior estimates 301
Chi-squared discrepancy measure 102
Chib and Carlin’s algorithm, conditional distribution sampling 122 129
| Chib and Carlin’s method, Gibbs sampler variable selection strategies 275
Chib’s method, marginal likelihood 127
Classical approach, classification trees 367
Classical estimation procedures GLMs 5
Classical logistic regression model vs. logistic regression estimate 224—225 225f
Classical MARS, semiparametric generalized linear models 221—222
Classical measurement error, WinBUGS 398—399
Classification and regression trees (CART) 221
Classification trees 365—371
Classification trees, basis functions 366—367
Classification trees, basis functions, example 366f
Classification trees, Bayesian approach 368—369
Classification trees, Bayesian partition model 371
Classification trees, birth step 368
Classification trees, breast cancer 369—371 370f 371t
Classification trees, classical approach 367
Classification trees, death step 368
Classification trees, example 369—371
Classification trees, Poison prior 368
Clinical indications, development 197
Closed forms, small area inference 97
Clustered binary outcome models 123
Common logit 236
Compatible vs. functionally compatible 29
Complementary log-log link 390
Complete hierarchical centering reparameterization technique 48 51
Compositional data, Bayesian analysis 349—362
Compositional data, Bayesian analysis, parametric approach 351—352
Compositional data, Bayesian analysis, posterior distributions and estimation 355—359
Compositional data, Bayesian analysis, results 359—361 360t—361t
Compositional data, Bayesian analysis, semiparametric approach 354—355
Compositional data, Bayesian analysis, simulation based model determination 352—354
Conditional autoregressive (CAR) model, Besag 28
Conditional autoregressive (CAR) model, sample paths 32f
Conditional distribution sampling, Chib and Carlin algorithm 122 129
Conditional distribution, ability parameters, item response modeling 184
Conditional distribution, item parameters, item response modeling 185
Conditional distribution, latent variables, item response modeling 184
Conditional independence structures, Bayesian graphical models 387—389
Conditional latency distributions 255
Conditional marginal density estimation (CDME), time series count data 167
Conditional models, correlated binary data diagnostics 314—315
Conditional models, correlated binary data diagnostics, posterior computations, correlated binary data diagnostics 318
Conditional posterior distributions, small area inference 97 99
Conditional predictive ordinate, OGLMs 81
Consensus panel 196
Contingency table, Gibbs sampler variable selection 278—281
Convergence, WinBUGS 401
Convex credible regions 359—360
Coronary angiography, post AMI practice guideline development 198—199 205—208 206f—207f
Coronary angiography, post AMI practice guideline development, likelihood 209f
Correlated binary data diagnostics 313—326
Correlated binary data diagnostics, model adequacy for data 320—324
Correlated binary data diagnostics, model adequacy for data, posterior predictive comparison 322—323
Correlated binary data diagnostics, model adequacy for data, simulation based model checking 323—324
Correlated binary data diagnostics, models 314—316
Correlated binary data diagnostics, models, conditional 314—315
Correlated binary data diagnostics, models, MVP 315
Correlated binary data diagnostics, models, MVT 315—316
Correlated binary data diagnostics, models, stratified and mixture 314
Correlated binary data diagnostics, posterior computations 317—320
Correlated binary data diagnostics, posterior computations, conditional models 318
Correlated binary data diagnostics, posterior computations, MVP 318—320
Correlated binary data diagnostics, posterior computations, MVT 320
Correlated binary data diagnostics, posterior computations, stratified and mixture models 317
Correlated binary data diagnostics, prior distributions 316—317
Correlated binary data diagnostics, voter behavior data 324—325
Correlated binary data, Bayesian method 113—129
Correlated binary data, Bayesian method, longitudinal binary data 119—123
Correlated binary data, Bayesian method, multivariate probit model 114—119
Correlated ordinal data models, Bayesian analysis 133—155
Correlated ordinal data models, Bayesian analysis, item response data example 148—155
Correlated ordinal data models, Bayesian analysis, model comparisons 143—146
Correlated ordinal data models, Bayesian analysis, model determination 142—148
Correlated ordinal data models, Bayesian analysis, model diagnostics 146—148
Correlated ordinal data models, Bayesian analysis, models 135—137
Correlated ordinal data models, Bayesian analysis, posterior computations 138—142
Correlated ordinal data models, Bayesian analysis, prior distributions 138
Correlated random effects 26—29
Correlated random effects, GLMMs 392—393
Correlation matrix, direct specification 26—27
Count data, time series, Bayesian methods 159—171
Covariance structure, model adequacy 15
Covariate effects, multivariate probit (MVP) 118t
Covariate measurement error, WinBUGS 397—400
Cox variable selection 287—309
Cox variable selection, computational implementation 299—305
Cox variable selection, computational implementation, data marginal distribution 299—301
Cox variable selection, computational implementation, posterior distribution sampling 302—305
Cox variable selection, method 289—299
Cox variable selection, method, baseline hazard rate prior distribution 289—292
Cox variable selection, method, likelihood function 292—293
Cox variable selection, method, model and notation 289
Cox variable selection, method, model space prior distribution 297—299
Cox variable selection, method, regression coefficient prior distribution 293—297
Cox variable selection, simulation study 305—308
Cox’s partial likelihood, proportional hazards regression models 287
Cross-validation approach, OGLMs 80
Cumulative baseline hazard rate model 289—290
Curves and surfaces models 220—221
Data adaptive Bayesian analysis, binary regression model 244—248
Data adaptive robust link functions, binary regression 243—251
Data marginal distribution, Cox variable selection 299—301
Death step, classification trees 368
Dependence structures, correlated binary data 116
Dependent variable logit, small area inference 92
Deprivation score, histogram 336f
Deprivation score, spatial variation 335f
Deterministic component 389
Deterministic error model, systemic part h modeling 220
Deviance information criteria (DIG) 16 402
Diabetes, predicted probability 227f
Difficulty parameters, item response modeling 176
Difficulty parameters, posterior scatterplot 188f
Directed acyclic graph (DAG), GLM 387 388f
Directed acyclic graph (DAG), GLM, extensions 393f
Dirichlet Process (DP) 10 11
Dirichlet Process (DP), model determination 83—84
Dirichlet Process (DP), OGLMs 81
Dirichlet Process mixed generalized linear models (DPMGLMs) 14
Dirichlet Process mixed generalized linear models (DPMGLMs), OGLMs 81—84
Dirichlet Process mixed overdispersed generalized linear models (DPMOGLMs), OGLMs 81—84
Dirichlet process prior 219
Dirichlet process prior, binary response regression 234—236
Dirichlet process prior, compositional data 349—360
Dirichlet process prior, errors-in-variables 341
Discrimination parameters, item response modeling 176 180
Disease mapping 90
Disease mapping, small area inference 94
Dispersion parameter, OGLMs 77
distribution 274
Distribution, system errors 60
Distributional assumptions, GLMs 4
Disturbances, set of 58
DoodleBUGS, GLM 389—390
Double-exponential families 12
Double-exponential families, OGLMs 76
Drop-outs, longitudinal binary data 127
Dynamic generalized linear models (DGLMs) 57—70
Dynamic generalized linear models (DGLMs), applications 65—70
Dynamic generalized linear models (DGLMs), definition 59—60
Dynamic linear models (DLM) 58—59
Efron’s model 13
EM algorithm 256
EM algorithm, mixture-model approach example 262—263
Empirical Bayes (EB) approaches 90
Empirical Bayes (EB) approaches, Bayesian approaches 339—341
Empirical Bayes (EB) approaches, Bayesian approaches, framework 339—340
Empirical Bayes (EB) approaches, Bayesian approaches, implementation 340
Empirical Bayes (EB) approaches, Bayesian approaches, previous work 340—341
|
|
|
| Ðåêëàìà |
|
|
|
|
|
|
|
|
Î ïðîåêòå