obj_fun_pimom {BayesS5}

the log posterior distribution based on piMoM priors and inverse gamma prior (0.01,0.01)

Description

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

Usage

obj_fun_pimom(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_pemom