Name
Title | ||
|---|---|---|
Approximate Predictive Densities and Their Applications in Generalized Linear Models. |
Abstract | ||
|---|---|---|
Exact calculations of model posterior probabilities or related quantities are often infeasible due to the analytical intractability of predictive densities. Here new approximations to obtain predictive densities are proposed and contrasted with those based on the Laplace method. Our theory and a numerical study indicate that the proposed methods are easy to implement, computationally efficient, and accurate over a wide range of hyperparameters. In the context of GLMs, we show that they can be employed to facilitate the posterior computation under three general classes of informative priors on regression coefficients. A real example is provided to demonstrate the feasibility and usefulness of the proposed methods in a fully Bayes variable selection procedure. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.csda.2010.11.005 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
approximate predictive density,normal prior,logistic regression,conjugate prior,predictive density,asymptotic normality,generalized linear model,analytical intractability,bayes variable selection procedure,informative prior,model posterior probability,general class,power prior,posterior computation,glm,laplace approximation,exact calculation,laplace method,general linear model,variable selection,posterior probability | Econometrics,Feature selection,Hyperparameter,Laplace's method,Posterior probability,Generalized linear model,Statistics,Prior probability,Conjugate prior,Mathematics,Bayes' theorem | Journal |
Volume | Issue | ISSN |
55 | 4 | 0167-9473 |
Citations | PageRank | References |
1 | 0.67 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Chen | 1 | 11 | 2.60 |
| Xinlei Wang | 2 | 228 | 16.47 |