ind_fun_NLfP

the index of covariates in a model

ind2

the degree of freedom for the B-spline basis

the projection matrix on the null space; Q matrix in Shin and Bhattacharya (2020+)

IP.phi

the logarithm of the normalizing constant of the nonlocal funcitonal prior

C.prior1

tuning

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

In p >> n settings, full posterior sampling using existing Markov chain Monte
Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical
perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya.

ind_fun_NLfP: the log-marginal likelhood function based on the invers moment functional priors and inverse gamma prior (0.01,0.01)

Description

Usage

Arguments

References