advanced Bayesian GLM applications
Bayesian Variable Selection
binary response modeling
Category=PBT
Chib's Method
Conditional Posterior Distributions
Cumulative Distribution Function
Data Sets
Dirichlet Process
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Full Conditional
Full Conditional Distributions
Generalized Linear Models
Gibbs Sampling
GLMM
Joint Posterior Distribution
latent variable analysis
longitudinal data analysis
Markov Chain Monte Carlo Algorithms
Mars Model
MCMC
MCMC Algorithm
MCMC Method
Mixture Model
Multivariate Probit Models
overdispersion methods
Posterior Distribution
Posterior Model Probabilities
Posterior Predictive Distribution
Prior Distribution
Prior Model Probabilities
Scale Mixture
semiparametric regression
statistical inference
Product details
- ISBN 9780367398606
- Weight: 820g
- Dimensions: 174 x 246mm
- Publication Date: 01 Nov 2019
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Paperback
Secure
checkout
Fast Shipping
Easy returns
This volume 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. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary response data, and identifies areas of important future research and new applications of GLMs.
Dipak K. Dey, Sujit K. Ghosh , Bani K. Mallick
