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.10. Item. Response. Modeling. James. Albert. Malay. Ghosh. ABSTRACT This chapter introduces the Bayesian fitting and checking ... The one and two parameter item response models are described and the models are illustrated using a mathematics placement exam. ... the ACT and their high school grade point average to recommend the best mathematics class for enrollment in the fall semester. ... One is interested in placing items on the exam which have different levels of difficulty.
|Title||:||Generalized Linear Models|
|Author||:||Dipak K. Dey, Sujit K. Ghosh, Bani K. Mallick|
|Publisher||:||CRC Press - 2000-05-25|