Multivariate Missing Data Handling with Iterative Bayesian Additive Lasso (IBAL) Multiple Imputation in Multicore Environment on Cloud

Abstract

Dealing with high dimensional data of the form p>n for multivariate analysis of missingness is very complicated. It arises in many fields mainly in social science, economics and medical study; genome is an example for that where is to mention that samples are very less compared to study elements nothing but variables. The analysis is a combination of large covariate vectors with response and non-response effects of unknown functional form related to response variable of interest. Thus, there is a need for regularized regression models, with effect of smoothing parametric method to do this in this work combine regularization by incorporating different types of covariates. Although regularization approaches fits to framework but the computation high demands in high dimensional analysis they also rely on penalized estimation. The solution is to implement regularization in iteration based smoothing approaches to fit such analysis. The proposed algorithm called Iterative Bayesian Additive Lasso (IBAL) is compared with standard methods in medical analysis and produced unbiased results. The overall work done in multi core environment offered by Cloud Service called Microsoft Azure. The performance is estimated with benchmarks like Standard Error (SE), Mean Square Error (MSE), and Confidence Interval (CI).