Leverage Subsampling for Joint Mean and Variance Models with Massive Data

In the era of massive data, how to obtain valuable information from large-scale data has become an important research direction. Subsampling is a common method to reduce the dimension of such data and improve the efficiency of model calculation. The subsampling based on the Joint Mean and Variance Models (JMVM) are proposed. Numerical simulation is used to evaluate uniform sampling (UNIF), leverage subsampling (LEV) and shrinkage leverage subsampling (SLEV). The results show that the models based on LEV and SLEV exhibit higher accuracy in the estimation of mean parameters.