  Big data is ubiquitous in practices, and it has also led to heavy computation
burden. To reduce the calculation cost and ensure the effectiveness of
parameter estimators, an optimal subset sampling method is proposed to estimate
the parameters in marginal models with massive longitudinal data. The optimal
subsampling probabilities are derived, and the corresponding asymptotic
properties are established to ensure the consistency and asymptotic normality
of the estimator. Extensive simulation studies are carried out to evaluate the
performance of the proposed method for continuous, binary and count data and
with four different working correlation matrices. A depression data is used to
illustrate the proposed method.
