Ghosh Sujit K., Mallick Bani K., Dey Dipak K. — Generalized Linear Models: A Bayesian Perspective :: Электронная библиотека попечительского совета мехмата МГУ

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Ghosh Sujit K., Mallick Bani K., Dey Dipak K. — Generalized Linear Models: A Bayesian Perspective

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Название: Generalized Linear Models: A Bayesian Perspective

Авторы: Ghosh Sujit K., Mallick Bani K., Dey Dipak K.

Аннотация:

Describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation, covering random effects in generalized linear mixed models (GLMMs) with explained examples. Considers parametric and semiparametric approaches to overdispersed GLMs, applies Bayesian GLMs to US mortality data, and presents methods of analyzing correlated binary data using latent variables. Describes and analyzes item response modeling for categorical data, and provides variable selection methods using the Gibbs sampler for Cox models. Dey is professor and head of the department of statistics at the University of Connecticut-Storrs

Язык:

Рубрика: Математика/Вероятность/Статистика и приложения/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 2000

Количество страниц: 442

Добавлена в каталог: 28.05.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID

Предметный указатель

Splitting nodes      366—369
Splitting questions      366—367
Standard categorization, AMI guideline compliance      208f
Standard deviation, residential properties dataset      382t
Standard errors, regression coefficients      308t
Standard of care, defined, post AMI guidelines      196 199—200
State space models      see “Dynamic linear models (DLM)”
Stochastic nodes      403
Stochastic Search Variable Selection (SSVS), Gibbs sampler variable selection strategies      276
Stochastic Search Variable Selection (SSVS), graphical model representation      279f
Stochastic Search Variable Selection (SSVS), posterior model probabilities      280t 281t
Stratified models, correlated binary data diagnostics      314
Stratified models, posterior computations, correlated binary data diagnostics      317
Strongly correlated random effects      29—30
Structural assumptions, GLMs      4
Student retention, University of Arkansas      237—239
Student-t specification, correlated binary data      116
Success parameters      5
Sun model      26
System errors, distribution      60
Systematic component      218
Systemic part h modeling      219—220
Systemic part h modeling, deterministic error model      220
Systemic part h modeling, random effects model      220
Tail area comparison      14
terminal nodes      366—369
Theorems, Bayes’      59
Theorems, hierarchical GLMMs      31—36
Theorems, improper priors, item response modeling      178—179
Theorems, prior distributions      46—47
Three-stage hierarchical multinominial-Dirichlet model, small area inference      99
Time series count data, Bayesian methods      159—171
Time series count data, Bayesian methods, example      167—171
Time series count data, Bayesian methods, likelihood functions      160—162
Time series count data, Bayesian methods, methods      160—165
Time series count data, Bayesian methods, model probability computations      165—167
Toeplitz correlation structure, multivariate probit (MVP)      126t
Total Pearson Discrepancy Measures, voter behavior data      325f
Tribolium castaneum data set      262t 267t 268t
Two-parameter exponential family model      12—14
Two-parameter exponential family model, item response modeling, example      188—191

Two-parameter exponential family model, item response modeling, Gibbs sampler      181—182
U.S. mortality data, models      100—102
Uncertain borrowing      103
Unclear points      226 227f
Unconditional priors      277
Unconditional priors, Gibbs sampler variable selection strategies      277
Univariate Poisson      60
University of Arkansas student retention      237—239
University of Arkansas student retention, finite mixture model (MF)      238
University of Arkansas student retention, general mixture model (MG)      238
University of Arkansas student retention, general mixture model vs. finite mixture model      237—238
University of Arkansas student retention, maximum likelihood logistic regression analysis      238
University of Arkansas student retention, model diagnostics      240f
University of Arkansas student retention, SSE      239t
Variable selection, generalized linear mixed models (GLMMs)      41—52
Variable selection, GLM      288
Variable selection, GLMMs study      51—52
Variograms, latent detrended plot, residential properties dataset      383f
Variograms, point-referenced binary spatial data, indicator      374
Variograms, point-referenced binary spatial data, modeling      374
Voter behavior data, correlated binary data diagnostics      324—325
Weibull densities      305
Weibull distribution      390
Wheeze data, models      341t
WinBUGS, classical measurement error      398—399
WinBUGS, convergence      401
WinBUGS, covariate measurement error      397—400
WinBUGS, extending      402—404
WinBUGS, GLM      389—391
WinBUGS, GLMMs      392—393
WinBUGS, Markov chain Monte Carlo (MCMC) methods      390—391
WinBUGS, model checking      402
WinBUGS, ordered categories      395—396
WinBUGS, over-parameterized models      399—400
WinBUGS, polytomous responses      394—395
WinBUGS, posterior sample size      401
WinBUGS, prior specification      400—401
WinBUGS, website      403
Wishart distribution, correlated ordinal data models      138—139
Wishart prior assumptions      117
Zeger — Karim formulation      8
Zellner’s g-priors      296

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