Multiple Imputation (MI) is always challenging in high dimensional settings. The imputation model with some selected number of predictors can be incompatible the analysis leading to inconsistent and biased estimates. Although compatibility such cases may not achieved, but one obtain consistent unbiased estimates using a semi-compatible model. We propose relax lasso penalty for selecting large s...

We propose a transition model for analysing data from complex longitudinal studies. Because missing values are practically unavoidable in large longitudinal studies, we also present a two-stage imputation method for handling general patterns of missing values on both the outcome and the covariates by combining multiple imputation with stochastic regression imputation. Our model is a time-varyin...

In real applications, massive data with graph structures are often incomplete due to various restrictions. Therefore, imputation algorithms have been widely used in the fields of social networks, sensor and MRI solve completion problem. To keep relevant, a structure is represented by graph-tensor, which each matrix vertex value weighted graph. The convolutional algorithm has proposed low-rank g...