-
作者：Yu, Tao; Qin, Jing; Li, Pengfei
作者单位：National University of Singapore; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); University of Waterloo
摘要：Multivariate mixture data analysis presents numerous challenges and constitutes a vital area of interest in the fields of statistics and data science. Research into multivariate mixture structures holds relevance across diverse application domains and plays a pivotal role in the advancement of artificial intelligence and machine learning. In this article, we focus on nonparametric estimation techniques for multivariate mixture data. Specifically, we assume a known number of subpopulations and ...
-
作者：Lee, Seong Jin; Sun, Will Wei; Liu, Yufeng
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Purdue University System; Purdue University; University of Michigan System; University of Michigan
摘要：As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features to continuously optimize assortments over time. In this article, we consider the dynamic assortment problem with dual contexts-user and item features. In high-dimensional scenarios, the quadratic growth of dimensions complicates comput...
-
作者：Choi, Jungjun; Kwon, Hyukjun; Liao, Yuan
作者单位：University of Rhode Island; Princeton University; Rutgers University System; Rutgers University New Brunswick
摘要：This article studies the inference about linear functionals of high-dimensional low-rank matrices. While most existing inference methods would require consistent estimation of the true rank, our procedure is robust to rank misspecification, making it a promising approach in applications where rank estimation can be unreliable. We estimate the low-rank spaces using pre-specified weighting matrices, known as diversified projections. A novel statistical insight is that, unlike the usual statistic...
-
作者：Perry, Ronan; Panigrahi, Snigdha; Bien, Jacob; Witten, Daniela
作者单位：University of Washington; University of Washington Seattle; University of Michigan System; University of Michigan; University of Southern California; University of Washington; University of Washington Seattle
摘要：Principal component analysis (PCA) is a longstanding approach for dimension reduction. It rests upon the assumption that the underlying signal has low rank, and thus can be well-summarized using a small number of dimensions. The output of PCA is typically represented using a scree plot, which displays the proportion of variance explained (PVE) by each principal component. While the PVE is extensively reported in routine analyses, to the best of our knowledge the notion of inference on the PVE ...
-
作者：Kang, Seungwoo; Oh, Hee-Seok
作者单位：Seoul National University (SNU); Seoul National University (SNU)
摘要：A new measure, L-1 centrality, is proposed to assess the centrality of vertices in an undirected and connected graph. The proposed measure can adequately handle graphs with weights assigned to vertices and edges. This study provides tools for graphical and multiscale analysis based on the L-1 centrality. Specifically, the suggested analysis tools include the target plot, L-1 centrality-based neighborhood, and local L-1 centrality. Most importantly, our work is closely associated with the conce...
