Rejoinder: “Co-citation and Co-authorship Networks of Statisticians”

Abstract

We thank David Donoho for very encouraging comments. As always, his penetrating vision and deep thoughts are extremely stimulating. We are glad that he summarizes a major philosophical difference between statistics in earlier years (e.g., the time of Francis Galton) and statistics in our time by just a few words: data-first versus model-first. We completely agree with his comment that “each effort by a statistics researcher to understand a newly available type of data enlarges our field; it should be a primary part of the career of statisticians to cultivate an interest in cultivating new types of datasets, so that new methodology can be discovered and developed”; these are exactly the motivations underlying our (several-year) efforts in collecting, cleaning, and analyzing a large-scale high-quality dataset. We would like to add that both traditions have strengths, and combining the strengths of two sides may greatly help statisticians deal with the so-called crisis of the 21st century in statistics we face today. Let us explain the crisis above first. In the model-first tradition, with a particular application problem in mind, we propose a model, develop a method and justify its optimality by some hard-to-prove theorems, and find a dataset to support the approach. In this tradition, we put a lot of faith on our model and our theory: we hope the model is adequate, and we hope our optimality theory warrants the superiority of our method over others. Modern machine learning literature (especially the recent development of deep learning) provides a different approach to justifying the “superiority” of an approach; we compare the proposed approach with existing approaches by the real data results over a dozen of benchmark datasets. To choose an algorithm for their dataset, a practitioner does not necessarily need warranties from a theorem; a superior performance over many benchmark datasets says it all. To some theoretical statisticians, this is rather disappointing, as they come from a long