Generalized Linear Models : A Bayesian Perspective

By Dipak K. Dey, Sujit K. Ghosh, Bani K. Mallick. 2000

155 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/053233383eaebc16df206328baecff68197a4430

Citations in the corpus

• A skew item response model (2006, unknown): “Such models are usually called the probit IRT model or ogive normal model (Albert 1992; Albert and Ghosh 2000) and logit IRT-model (see Birnbaum 1968), respectively.”
• Marginal Likelihood and Bayes Factors for Dirichlet Process Mixture Models (2003, unknown): “Albert and Chib (1993) rst showed how to t a t-link model, and Basu and Mukhopadhyay (2000) extended this idea to DPM link models.”
• Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (2009, unknown): “(a) Regression models: Bayesian generalized linear models correspond to the linear predictor ηi =α+Σnβk=1 βkzki (Dey et al., 2000).”
• Fire severity and landscape context effects on arboreal marsupials (2013, unknown): “To assess the effect of fire severity on the probability of the presence of a species or presence of any species after fire at the site level, we compared the rates of non-zero detections on each site using Bayesian logistic regression (Dey et al., 2000) as follows: Let Di indicate whether or not th”