1 citations · 2 across the 8 of their papers we have counts for
10 papers
One Proposal for Every Margin: Zero-Shot Amortized Sequential Importance Sampling for Binary Matrices
Ruishuo Chen, Weijia Li, Xun Wang +3
In ecology, psychometrics, and the analysis of social and financial networks, binary matrices are often analyzed conditional on their observed row and column sums, which restricts…
Asymptotic Anytime-Valid Quantile Inference under Local Differential Privacy
Leheng Cai, Qirui Hu, Shuyuan Wu
Sequential quantile inference is difficult under local differential privacy because every record is randomized before reaching the analyst and the limiting quantile variance depend…
Approximation Theorems for High-Dimensional Canonical U-Statistics: Gaussian Chaos and Phase Transition
Leheng Cai, Qirui Hu
We study simultaneous inference for maxima of canonical order-two -statistics in high dimension. Degeneracy makes quadratic fluctuations leading, so ordinary Gaussian calibratio…
Recursive Multiple Change Point Detection of Nonstationary Time Series: Instability Tests, Estimation and Confidence Intervals
Leheng Cai, Zhou Zhou
We develop bootstrap-assisted robust binary segmentation (BARBS), a recursive binary segmentation method for multiple change point detection under general nonstationary temporal dy…
Asymptotic Anytime-Valid Inference for U-statistics
Leheng Cai, Qirui Hu, Weijia Li
We study asymptotic anytime-valid confidence sequences for degree-two U-statistics under continuous monitoring. In the nondegenerate case, Hoeffding's projection reduces the proble…
Strong Gaussian approximation for U-statistics in high dimensions and beyond
Weijia Li, Leheng Cai, Qirui Hu
We establish a strong Gaussian approximation for high-dimensional non-degenerate U-statistics with diverging dimension. Under mild assumptions, we construct, on a sufficiently rich…
Federated Learning of Quantile Inference under Local Differential Privacy
Leheng Cai, Qirui Hu, Shuyuan Wu
In this paper, we investigate federated learning for quantile inference under local differential privacy (LDP). We propose an estimator based on local stochastic gradient descent (…
Unified theory of testing relevant hypotheses in functional time series
Leheng Cai, Qirui Hu
In this paper, we develop a {\em unified} framework for testing relevant hypotheses in functional time series. The proposed approach accommodates one-sample, two-sample, and change…
From sparse to dense functional time series: phase transitions of detecting structural breaks and beyond
Leheng Cai, Qirui Hu
We develop a novel methodology for detecting abrupt break points in mean functions of functional time series, adaptable to arbitrary sampling schemes. By employing B-spline smoothi…
From Sparse to Dense Functional Data: Phase Transitions from a Simultaneous Inference Perspective
Leheng Cai, Qirui Hu
We aim to develop simultaneous inference tools for the mean function of functional data from sparse to dense. First, we derive a unified Gaussian approximation to construct simulta…