A New Bayesian Single Index Model with or without Covariates Missing at Random.

ABSTRACT: For many biomedical, environmental, and economic studies, the single index model provides a practical dimension reaction as well as a good physical interpretation of the unknown nonlinear relationship between the response and its multiple predictors. However, widespread uses of existing Bayesian analysis for such models are lacking in practice due to some major impediments, including slow mixing of the Markov Chain Monte Carlo (MCMC), the inability to deal with missing covariates and a lack of theoretical justification of the rate of convergence of Bayesian estimates. We present a new Bayesian single index model with an associated MCMC algorithm that incorporates an efficient Metropolis-Hastings (MH) step for the conditional distribution of the index vector. Our method leads to a model wit

SUBMITTER: Dhara K 

PROVIDER: S-EPMC7943072 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature