ind_fun_pimom {BayesS5}

the log-marginal likelhood function based on piMoM priors

Description

a log-marginal likelhood value of a model, based on the piMoM prior on the regression coefficients and inverse gamma prior (0.01,0.01) on the variance.

Usage

ind_fun_pimom(X.ind,y,n,p,tuning)

Arguments

X.ind

the subset of covariates in a model

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.

Johnson, V. E. and Rossell, D. (2012) Bayesian model selection in high-dimensional settings , David, Journal of the American Statistical Association, 107 (498), 649-660.

See Also

ind_fun_g, ind_fun_pemom