obj_fun_pimom {BayesS5} | R Documentation |
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.
obj_fun_pimom(ind,X,y,n,p,tuning)
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 |
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.