The rapid advance in science and technology in the past decade brings an extraordinary amount of data that were inaccessible just a decade ago, offering researchers an unprecedented opportunity to tackle much larger and more complex research challenges. The opportunity, however, has not yet been fully utilized, because effective and efficient statistical and computing tools for analyzing super-large dataset are still lacking. One major challenge is that the advance of computing technologies still lags far behind the exponential growth of database.
In this talk, I will present an emerging family of statistical methods, called leveraging methods to facilitate scientific discoveries using limited computing resources. Leveraging methods are designed under a subsampling framework, in which one samples a small proportion of the data (subsample) from the full sample, and then performs intended computations for the full sample using the small subsample as a surrogate. The key to the success of the leveraging methods is to construct nonuniform sampling probabilities so that influential data points are sampled with high probabilities. These methods stand as a unique development of their type in big data analytics and allow pervasive access to massive amounts of information without resorting to high-performance computing and cloud computing.