Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data
This addresses computational bottlenecks for statisticians and data scientists handling massive datasets, offering a practical solution for distributed environments.
The paper tackles the problem of subsampling massive data when nonuniform probabilities cannot be computed all at once, by proposing optimal Poisson subsampling and a distributed framework, achieving efficient estimation with asymptotic guarantees.
Nonuniform subsampling methods are effective to reduce computational burden and maintain estimation efficiency for massive data. Existing methods mostly focus on subsampling with replacement due to its high computational efficiency. If the data volume is so large that nonuniform subsampling probabilities cannot be calculated all at once, then subsampling with replacement is infeasible to implement. This paper solves this problem using Poisson subsampling. We first derive optimal Poisson subsampling probabilities in the context of quasi-likelihood estimation under the A- and L-optimality criteria. For a practically implementable algorithm with approximated optimal subsampling probabilities, we establish the consistency and asymptotic normality of the resultant estimators. To deal with the situation that the full data are stored in different blocks or at multiple locations, we develop a distributed subsampling framework, in which statistics are computed simultaneously on smaller partitions of the full data. Asymptotic properties of the resultant aggregated estimator are investigated. We illustrate and evaluate the proposed strategies through numerical experiments on simulated and real data sets.