36 citations · 78 across the 20 of their papers we have counts for
21 papers
Noisy Matrix Completion under Informative Missingness
Xuanyu Chen, Xiaoyue Niu, Gongjun Xu +1
Noisy matrix completion is a fundamental problem in statistical learning and has attracted a substantial amount of interest over the last two decades. In a variety of applications,…
Interpreting Network Differential Privacy
Jonathan Hehir, Xiaoyue Niu, Aleksandra Slavkovic
How do we interpret the differential privacy (DP) guarantee for network data? We take a deep dive into a popular form of network DP (--edge DP) to find that many of it…
Estimating and Correcting Degree Ratio Bias in the Network Scale-up Method
Ian Laga, Jessica P. Kunke, Tyler H. McCormick +1
The Network Scale-up Method (NSUM) uses social networks and answers to "How many X's do you know?" questions to estimate sizes of groups excluded by standard surveys. This paper ad…
Comparing the Robustness of Simple Network Scale-Up Method (NSUM) Estimators
Jessica P. Kunke, Ian Laga, Xiaoyue Niu +1
The network scale-up method (NSUM) is a cost-effective approach to estimating the size or prevalence of a group of people that is hard to reach through a standard survey. The basic…
Perfect Spectral Clustering with Discrete Covariates
Jonathan Hehir, Xiaoyue Niu, Aleksandra Slavkovic
Among community detection methods, spectral clustering enjoys two desirable properties: computational efficiency and theoretical guarantees of consistency. Most studies of spectral…
A Correlated Network Scale-up Model: Finding the Connection Between Subpopulations
Ian Laga, Le Bao, Xiaoyue Niu
Aggregated relational data (ARD), formed from "How many X's do you know?" questions, is a powerful tool for learning important network characteristics with incomplete network data.…
Consistent Spectral Clustering of Network Block Models under Local Differential Privacy
Jonathan Hehir, Aleksandra Slavkovic, Xiaoyue Niu
The stochastic block model (SBM) and degree-corrected block model (DCBM) are network models often selected as the fundamental setting in which to analyze the theoretical properties…
A Bayesian hierarchical modeling approach to combining multiple data sources: A case study in size estimation
Jacob Parsons, Xiaoyue Niu, Le Bao
To combat the HIV/AIDS pandemic effectively, targeted interventions among certain key populations play a critical role. Examples of such key populations include sex workers, people…
Thirty Years of The Network Scale up Method
Ian Laga, Le Bao, Xiaoyue Niu
Estimating the size of hard-to-reach populations is an important problem for many fields. The Network Scale-up Method (NSUM) is a relatively new approach to estimate the size of th…
Evaluating the relative contribution of data sources in a Bayesian analysis with the application of estimating the size of hard to reach populations
Jacob Parsons, Xiaoyue Niu, Le Bao
When using multiple data sources in an analysis, it is important to understand the influence of each data source on the analysis and the consistency of the data sources with each o…
What Can We Learn from the Travelers Data in Detecting Disease Outbreaks -- A Case Study of the COVID-19 Epidemic
Le Bao, Ying Zhang, Xiaoyue Niu
Background: Travel is a potent force in the emergence of disease. We discussed how the traveler case reports could aid in a timely detection of a disease outbreak. Methods: Using t…
Modeling the Marked Presence-only Data: A Case Study of Estimating the Female Sex Worker Size in Malawi
Ian Laga, Xiaoyue Niu, Le Bao
Certain subpopulations like female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID) often have higher prevalence of HIV/AIDS and are difficult…
Near-Balanced Incomplete Block Designs with An Application to Poster Competitions
Xiaoyue Niu, James L. Rosenberger
Judging scholarly posters creates a challenge to assign the judges efficiently. If there are many posters and few reviews per judge, the commonly used Balanced Incomplete Block Des…
A Dynamic Additive and Multiplicative Effects Model with Application to the United Nations Voting Behaviors
Bomin Kim, Xiaoyue Niu, David R. Hunter +1
Motivated by a study of United Nations voting behaviors, we introduce a regression model for a series of networks that are correlated over time. Our model is a dynamic extension of…
A Review of Dynamic Network Models with Latent Variables
Bomin Kim, Kevin Lee, Lingzhou Xue +1
We present a selective review of statistical modeling of dynamic networks. We focus on models with latent variables, specifically, the latent space models and the latent class mode…
Incorporating Hierarchical Structure Into Dynamic Systems: An Application Of Estimating HIV Epidemics At Sub-National And Sub-Population Level
Le Bao, Ben Sheng, Xiaoyue Niu +4
Dynamic models have been successfully used in producing estimates of HIV epidemics at national level, due to their epidemiological nature and their ability to simultaneously estima…
Estimating HIV Epidemics for Sub-National Areas
Le Bao, Xiaoyue Niu, Mary Mahy +1
As the global HIV pandemic enters its fourth decade, increasing numbers of surveillance sites have been established which allows countries to look into the epidemics at a finer sca…
A Hierarchical Model for Estimating HIV Epidemics
Le Bao, Mary Mahy, Xiaoyue Niu +2
As the global HIV pandemic enters its fourth decade, increasing numbers of surveillance sites have been established which allows countries to look into the epidemics at a finer sca…
Joint Mean and Covariance Modeling of Multiple Health Outcome Measures
Xiaoyue Niu, Peter D. Hoff
Health exams determine a patient's health status by comparing the patient's measurement with a population reference range, a 95% interval derived from a homogeneous reference popul…
Information bounds for Gaussian copulas
Peter D. Hoff, Xiaoyue Niu, Jon A. Wellner
Often of primary interest in the analysis of multivariate data are the copula parameters describing the dependence among the variables, rather than the univariate marginal distribu…
A covariance regression model
Peter D. Hoff, Xiaoyue Niu
Classical regression analysis relates the expectation of a response variable to a linear combination of explanatory variables. In this article, we propose a covariance regression m…