On statistics, computation and scalability
This addresses the challenge of scalability in statistics for researchers and practitioners dealing with large datasets, but it appears incremental as it builds on existing computational perspectives.
The paper tackles the problem of designing statistical procedures that are computationally scalable to massive datasets while meeting time constraints, focusing on time-data tradeoffs through divide-and-conquer methods and convex relaxations.
How should statistical procedures be designed so as to be scalable computationally to the massive datasets that are increasingly the norm? When coupled with the requirement that an answer to an inferential question be delivered within a certain time budget, this question has significant repercussions for the field of statistics. With the goal of identifying "time-data tradeoffs," we investigate some of the statistical consequences of computational perspectives on scability, in particular divide-and-conquer methodology and hierarchies of convex relaxations.