DISTRIBUTED ESTIMATION AND INFERENCE FOR SEMIPARAMETRIC BINARY RESPONSE MODELS

The development of modern technology has enabled data collection of unprecedented size, which poses new challenges to many statistical estimation and inference problems. This paper studies the maximum score estimator of a semiparametric binary choice model under a distributed computing environment without prespecifying the noise distribution. An intuitive divideand-conquer estimator is computationally expensive and restricted by a nonregular constraint on the number of machines, due to the highly nonsmooth nature of the objective function. We propose (1) a one-shot divide-and-conquer estimator after smoothing the objective to relax the constraint, and (2) a multiround estimator to completely remove the constraint via iterative smoothing. We specify an adaptive choice of kernel smoother with a sequentially shrinking bandwidth to achieve the superlinear improvement of the optimization error over multiple iterations. The improved statistical accuracy per iteration is derived, and a quadratic convergence up to the optimal statistical error rate is established. We further provide two generalizations to handle the heterogeneity of data sets and high-dimensional problems where the parameter of interest is sparse.