22 citations · 84 across the 31 of their papers we have counts for
7 papers · 1 filter
Exact Cluster Recovery via Classical Multidimensional Scaling
Anna Little, Yuying Xie, Qiang Sun
Classical multidimensional scaling is an important dimension reduction technique. Yet few theoretical results characterizing its statistical performance exist. This paper provides…
User-Friendly Covariance Estimation for Heavy-Tailed Distributions
Yuan Ke, Stanislav Minsker, Zhao Ren +2
We offer a survey of recent results on covariance estimation for heavy-tailed distributions. By unifying ideas scattered in the literature, we propose user-friendly methods that fa…
Modified Multidimensional Scaling and High Dimensional Clustering
Xiucai Ding, Qiang Sun
Multidimensional scaling is an important dimension reduction tool in statistics and machine learning. Yet few theoretical results characterizing its statistical performance exist,…
Robust Sparse Reduced Rank Regression in High Dimensions
Kean Ming Tan, Qiang Sun, Daniela Witten
We propose robust sparse reduced rank regression for analyzing large and complex high-dimensional data with heavy-tailed random noise. The proposed method is based on a convex rela…
Bernstein's inequalities for general Markov chains
Bai Jiang, Qiang Sun, Jianqing Fan
We establish Bernstein's inequalities for functions of general (general-state-space and possibly non-reversible) Markov chains. These inequalities achieve sharp variance proxies an…
Hoeffding's lemma for Markov Chains and its applications to statistical learning
Jianqing Fan, Bai Jiang, Qiang Sun
We extend Hoeffding's lemma to general-state-space and not necessarily reversible Markov chains. Let be a stationary Markov chain with invariant measure and…
Principal component analysis for big data
Jianqing Fan, Qiang Sun, Wen-Xin Zhou +1
Big data is transforming our world, revolutionizing operations and analytics everywhere, from financial engineering to biomedical sciences. The complexity of big data often makes d…