  This paper develops an updatable inverse probability weighting (UIPW)
estimation for the generalized linear models with response missing at random in
streaming data sets. A two-step online updating algorithm is provided for the
proposed method. In the first step we construct an updatable estimator for the
parameter in propensity function and hence obtain an updatable estimator of the
propensity function; in the second step we propose an UIPW estimator with the
inverse of the updating propensity function value at each observation as the
weight for estimating the parameter of interest. The UIPW estimation is
universally applicable due to its relaxation on the constraint on the number of
data batches. It is shown that the proposed estimator is consistent and
asymptotically normal with the same asymptotic variance as that of the oracle
estimator, and hence the oracle property is obtained. The finite sample
performance of the proposed estimator is illustrated by the simulation and real
data analysis. All numerical studies confirm that the UIPW estimator performs
as well as the batch learner.
