4 citations · 7 across the 9 of their papers we have counts for
10 papers
Intersecting families of loose spanning hypertrees
Yury Person, Thomas Schweser
A loose hypertree is a hypergraph whose incidence graph is a tree. Let , , and . We determine the largest -intersecting families of lo…
Self-Normalized Inference for Constant-Stepsize Temporal-Difference Learning under Markovian Sampling
Min Zeng, Yichen Zhang, Xiaofeng Shao
Constant-stepsize temporal-difference (TD) learning is attractive for policy evaluation, but inference from a single Markov trajectory must account for serial dependence and a step…
Testing Equality of Conditional Distributions via Generative Models
Hanjia Gao, Linjun Huang, Yun Yang +1
We study the problem of testing whether two conditional distributions are equal using generative models. The proposed method learns a conditional generator from each sample and use…
Hypothesis Testing for a Functional Parameter via Self-normalization
Yi Zhang, Xiaofeng Shao
Testing simple or composite hypothesis on a functional parameter has attracted considerable attention in time series analysis. To accommodate for the unknown temporal dependence, c…
Another Look at Bandwidth-free Inference: a Sample Splitting Approach
Yi Zhang, Xiaofeng Shao
The bandwidth-free tests/inferences for a multi-dimensional parameter have attracted considerable attention in econometrics and statistics literature. These tests can be convenient…
Change-Point Detection for Object-valued Time Series
Yi Zhang, Changbo Zhu, Xiaofeng Shao
This article is concerned with change point detection for object-valued data that reside in a metric space, which has attracted some recent interests in statistics and econometrics…
Generalized Spectral Testing with Sample Splitting
Yuxin Tao, Feiyu Jiang, Xiaofeng Shao
Residual-based goodness-of-fit tests for parametric time-series models are often complicated by parameter-estimation effects, which can alter the limiting behavior of diagnostic st…
Resampling-free Inference for Time Series via RKHS Embedding
Deep Ghoshal, Xiaofeng Shao
In this article, we study nonparametric inference problems in the context of multivariate or functional time series, including testing for goodness-of-fit, the presence of a change…
Testing Conditional Mean Independence Using Generative Neural Networks
Yi Zhang, Linjun Huang, Yun Yang +1
Conditional mean independence (CMI) testing is crucial for statistical tasks including model determination and variable importance evaluation. In this work, we introduce a novel po…
Doubly Robust Conditional Independence Testing with Generative Neural Networks
Yi Zhang, Linjun Huang, Yun Yang +1
This article addresses the problem of testing the conditional independence of two generic random vectors and given a third random vector , which plays an important role…