60 citations · 145 across the 15 of their papers we have counts for
15 papers
Statistical Analysis of Multi-Relational Network Recovery
Zhi Wang, Xueying Tang, Jingchen Liu
In this paper, we develop asymptotic theories for a class of latent variable models for large-scale multi-relational networks. In particular, we establish consistency results and a…
Moderate Deviation for Random Elliptic PDEs with Small Noise
Xiaoou Li, Jingchen Liu, Jianfeng Lu +1
Partial differential equations with random inputs have become popular models to characterize physical systems with uncertainty coming from, e.g., imprecise measurement and intrinsi…
A Multilevel Approach towards Unbiased Sampling of Random Elliptic Partial Differential Equations
Xiaoou Li, Jingchen Liu
Partial differential equation is a powerful tool to characterize various physics systems. In practice, measurement errors are often present and probability models are employed to a…
Sequential Hypothesis Test with Online Usage-Constrained Sensor Selection
Shang Li, Xiaoou Li, Xiaodong Wang +1
This work investigates the sequential hypothesis testing problem with online sensor selection and sensor usage constraints. That is, in a sensor network, the fusion center sequenti…
Decentralized Sequential Composite Hypothesis Test Based on One-Bit Communication
Shang Li, Xiaoou Li, Xiaodong Wang +1
This paper considers the sequential composite hypothesis test with multiple sensors. The sensors observe random samples in parallel and communicate with a fusion center, who makes…
Efficient rare event simulation for failure problems in random media
Jingchen Liu, Jianfeng Lu, Xiang Zhou
In this paper we study rare events associated to solutions of elliptic partial differential equations with spatially varying random coefficients. The random coefficients follow the…
Total variation approximations and conditional limit theorems for multivariate regularly varying random walks conditioned on ruin
Jose Blanchet, Jingchen Liu
We study a new technique for the asymptotic analysis of heavy-tailed systems conditioned on large deviations events. We illustrate our approach in the context of ruin events of mul…
Rare-event Simulation and Efficient Discretization for the Supremum of Gaussian Random Fields
Xiaoou Li, Jingchen Liu
In this paper, we consider a classic problem concerning the high excursion probabilities of a Gaussian random field living on a compact set . We develop efficient computatio…
Extreme Analysis of a Non-convex and Nonlinear Functional of Gaussian Processes -- On the Tail Asymptotics of Random Ordinary Differential Equations
Jingchen Liu, Xiang Zhou
In this paper, we consider a stochastic system described by a differential equation admitting a spatially varying random coefficient. The differential equation has been employed to…
On the conditional distributions and the efficient simulations of exponential integrals of Gaussian random fields
Jingchen Liu, Gongjun Xu
In this paper, we consider the extreme behavior of a Gaussian random field living on a compact set . In particular, we are interested in tail events associated with the i…
Some Asymptotic Results of Gaussian Random Fields with Varying Mean Functions and the Associated Processes
Jingchen Liu, Gongjun Xu
In this paper, we derive tail approximations of integrals of exponential functions of Gaussian random fields with varying mean functions and approximations of the associated point…
On the Stationary Distribution of Iterative Imputations
Jingchen Liu, Andrew Gelman, Jennifer Hill +1
Iterative imputation, in which variables are imputed one at a time each given a model predicting from all the others, is a popular technique that can be convenient and flexible, as…
Efficient Simulation and Conditional Functional Limit Theorems for Ruinous Heavy-tailed Random Walks
Jose Blanchet, Jingchen Liu
The contribution of this paper is to introduce change of measure based techniques for the rare-event analysis of heavy-tailed stochastic processes. Our changes-of-measure are param…
Tail approximations of integrals of Gaussian random fields
Jingchen Liu
This paper develops asymptotic approximations of as for a homogeneous smooth Gaussian random field, , living on a compact -dime…
Efficient Monte Carlo for high excursions of Gaussian random fields
Robert J. Adler, Jose H. Blanchet, Jingchen Liu
Our focus is on the design and analysis of efficient Monte Carlo methods for computing tail probabilities for the suprema of Gaussian random fields, along with conditional expectat…