2 citations · 3 across the 6 of their papers we have counts for
7 papers
Conditional Mean Independence, Global Sensitivity Analysis, and Variable Screening Using nearest neighbor Graphs
Anirban Chatterjee, Ziang Niu, Bhaswar B. Bhattacharya
Quantifying how much of the variation in a response is explained by its conditional mean is central to many statistical problems. In this paper, we develop a unified framework for…
A Kernel-Based Conditional Two-Sample Test Using Nearest Neighbors (with Applications to Calibration, Regression Curves, and Simulation-Based Inference)
Anirban Chatterjee, Ziang Niu, Bhaswar B. Bhattacharya
In this paper we introduce a kernel-based measure for detecting differences between two conditional distributions. Using the `kernel trick' and nearest-neighbor graphs, we propose…
Distribution-free joint independence testing and robust independent component analysis using optimal transport
Ziang Niu, Bhaswar B. Bhattacharya
In this paper we study the problem of measuring and testing joint independence for a collection of multivariate random variables. Using the emerging theory of optimal transport (OT…
Reconciling model-X and doubly robust approaches to conditional independence testing
Ziang Niu, Abhinav Chakraborty, Oliver Dukes +1
Model-X approaches to testing conditional independence between a predictor and an outcome variable given a vector of covariates usually assume exact knowledge of the conditional di…
Estimation and inference for high-dimensional nonparametric additive instrumental-variables regression
Ziang Niu, Yuwen Gu, Wei Li
The method of instrumental variables provides a fundamental and practical tool for causal inference in many empirical studies where unmeasured confounding between the treatments an…
High Dimensional Logistic Regression Under Network Dependence
Somabha Mukherjee, Ziang Niu, Sagnik Halder +2
Logistic regression is key method for modeling the probability of a binary outcome based on a collection of covariates. However, the classical formulation of logistic regression re…
Discrepancy-based Inference for Intractable Generative Models using Quasi-Monte Carlo
Ziang Niu, Johanna Meier, François-Xavier Briol
Intractable generative models are models for which the likelihood is unavailable but sampling is possible. Most approaches to parameter inference in this setting require the comput…