Variable Selection In Linear Model With Missing Data

We will consider variable selection in the linear model, in the case of missing data. At first, we will select variables on complete observations via Least angle regression and BIC criteria. Our simulation studies suggest that this method is stable and save time. Then we use least square estimates to establish regression equations for all the data, We use prediction to input missing data and use LARS and BIC to select variables.