obj_fun_pemom: the log posterior distribution based on peMoM priors and...

In BayesS5: Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5)

Description

a log posterior density value at regression coefficients of a model, based on the peMoM prior on the regression coefficients and inverse gamma prior (0.01,0.01) on the variance.

Usage

obj_fun_pemom(ind,X,y,n,p,tuning)

Arguments

ind	the index set of a model
X	the covariates
y	the response variable
n	the sample size
p	the total number of covariates
tuning	a value of the tuning parameter

References

Shin, M., Bhattacharya, A., Johnson V. E. (2018) A Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings, Statistica Sinica.

Rossell, D., Telesca, D., and Johnson, V. E. (2013) High-dimensional Bayesian classifiers using non-local priors, Statistical Models for Data Analysis, 305-313.

See Also

obj_fun_g, obj_fun_pimom

BayesS5 documentation built on March 26, 2020, 7:14 p.m.