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Motivated by the recent active research on online analytical processing (OLAP), we develop a computation and storage efficient algorithm for estimating equation (EE) estimation in massive data sets using a "divide-and-conquer" strategy. In each partition of the data set, we compress the raw data into some low dimensional statistics and then discard the raw data. Then, we obtain an approximation to the EE estimator, the aggregated EE (AEE) estimator, by solving an equation aggregated from the saved low dimensional statistics in all partitions. Such low dimensional statistics are taken as the EE estimates and first-order derivatives of the estimating equations in each partition.
Lin et al. (Sat,) studied this question.