A0736
Title: Inference for high-dimensional exchangeable arrays with an application to network data
Authors: Kengo Kato - Cornell University (United States) [presenting]
Yuya Sasaki - Vanderbilt University (United States)
Harold Chiang - University of Wisconsin-Madison (United States)
Abstract: Inference is considered for high-dimensional separately and jointly exchangeable arrays where the dimensions may be much larger than the sample sizes. For both exchangeable arrays, we first derive high-dimensional central limit theorems over the rectangles and subsequently develop novel multiplier bootstraps with theoretical guarantees. These theoretical results rely on new technical tools such as Hoeffding-type decomposition and maximal inequalities for the degenerate components in the Hoeffiding-type decomposition for the exchangeable arrays. We exhibit applications of our methods to uniform confidence bands for density estimation under joint exchangeability and penalty choice for L1-penalized regression under separate exchangeability. Extensive simulations demonstrate precise uniform coverage rates. We illustrate by constructing uniform confidence bands for international trade network densities.
