A1164
Title: Incorporating auxiliary information for improved statistical inference and its extensions to distributed algorithms
Authors: Miaomiao Yu - East China Normal University (China) [presenting]
Abstract: Personal credits have always been a hot topic in the society. Among all of them, the evaluation of default risk is particularly concerning since robust estimation based on personal information can help needy individuals get loans and financial institutions to avoid losses. So far, there have been no good solutions due to limited data, especially default information. With the advent of the era of big data, it is possible to improve the effectiveness of estimates by using auxiliary information from external studies or public domains. However, individual-level data can not be gained directly because of the emphasis on data privacy; that is, only some summarized statistics with auxiliary information are allowed to be shared. To effectively utilize external integrated auxiliary information to improve the accuracy of default risk estimation, a unified auxiliary information framework is introduced, which is referred to as the enhanced GEE method, to effectively incorporate various external summary results by employing the generalized estimating equations (GEE) approach and augmenting a weighted logarithm of confidence density on GEE function. Besides, a low-cost Map-Reduce procedure for the distributed statistical inference of enhanced GEE method in big data is developed that can achieve the same efficiency as the oracle-enhanced GEE approach under mild conditions.
