10 papers
Belted Engression: Sufficient Dimension Reduction for Generative Distributional Regression
Wenxi Tan, Bing Li, Lingzhou Xue
Modern conditional generative models face significant challenges when learning complex covariate dependencies. While sufficient dimension reduction (SDR) provides a principled appr…
Distance Profile Embedding for Independence and Conditional Independence Testing of Random Objects
Wenxi Tan, Bing Li, Lingzhou Xue
Testing independence or conditional independence is fundamental to statistical inference, yet existing methods for non-Euclidean random objects often face a difficult trade-off bet…
A Test for Treatment Heterogeneity under a Distributional Difference-in-Difference Framework
Satarupa Bhattacharjee, Bing Li, Lingzhou Xue
We develop a novel distributional Difference-in-Differences (DiD) framework to capture treatment heterogeneity across outcome distributions. By leveraging optimal transport, we use…
A Unified Framework for Nonlinear Mediation Analysis of Random Objects
Wenxi Tan, Bing Li, Lingzhou Xue
Mediation analysis for complex, non-Euclidean data, such as probability distributions, compositions, images, and networks, presents significant methodological challenges due to the…
Structure-Preserving Nonlinear Sufficient Dimension Reduction for Tensors
Dianjun Lin, Bing Li, Lingzhou Xue
We introduce two nonlinear sufficient dimension reduction methods for regressions with tensor-valued predictors. Our goal is two-fold: the first is to preserve the tensor structure…
Collapsing Categories for Regression with Mixed Predictors
Chaegeun Song, Zhong Zheng, Bing Li +1
Categorical predictors are omnipresent in everyday regression practice: in fact, most regression data involve some categorical predictors, and this tendency is increasing in modern…
Variable Selection for Additive Global Fréchet Regression
Haoyi Yang, Satarupa Bhattacharjee, Lingzhou Xue +1
We present a novel framework for variable selection in Fréchet regression with responses in general metric spaces, a setting increasingly relevant for analyzing non-Euclidean data…
Doubly robust estimation of causal effects for random object outcomes with continuous treatments
Satarupa Bhattacharjee, Bing Li, Xiao Wu +1
Causal inference is central to statistics and scientific discovery, enabling researchers to identify cause-and-effect relationships beyond associations. While traditionally studied…
A Copula Graphical Model for Multi-Attribute Data using Optimal Transport
Qi Zhang, Bing Li, Lingzhou Xue
Motivated by modern data forms such as images and multi-view data, the multi-attribute graphical model aims to explore the conditional independence structure among vectors. Under t…
Nonlinear global Fréchet regression for random objects via weak conditional expectation
Satarupa Bhattacharjee, Bing Li, Lingzhou Xue
Random objects are complex non-Euclidean data taking value in general metric space, possibly devoid of any underlying vector space structure. Such data are getting increasingly abu…