110 citations · 630 across the 32 of their papers we have counts for
10 papers · 1 filter
Edge differentially private estimation in the -model via jittering and method of moments
Jinyuan Chang, Qiao Hu, Eric D. Kolaczyk +2
A standing challenge in data privacy is the trade-off between the level of privacy and the efficiency of statistical inference. Here we conduct an in-depth study of this trade-off…
Factor Modelling for Clustering High-dimensional Time Series
Bo Zhang, Guangming Pan, Qiwei Yao +1
We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure. Each cluster is characterized by its own cluster-spec…
On testing for high-dimensional white noise
Zeng Li, Clifford Lam, Jianfeng Yao +1
Testing for white noise is a classical yet important problem in statistics, especially for diagnostic checks in time series modeling and linear regression. For high-dimensional tim…
High dimensional stochastic regression with latent factors, endogeneity and nonlinearity
Jinyuan Chang, Bin Guo, Qiwei Yao
We consider a multivariate time series model which represents a high dimensional vector process as a sum of three terms: a linear regression of some observed regressors, a linear c…
Identifying the finite dimensionality of curve time series
Neil Bathia, Qiwei Yao, Flavio Ziegelmann
The curve time series framework provides a convenient vehicle to accommodate some nonstationary features into a stationary setup. We propose a new method to identify the dimensiona…
Factor modeling for high-dimensional time series: Inference for the number of factors
Clifford Lam, Qiwei Yao
This paper deals with the factor modeling for high-dimensional time series based on a dimension-reduction viewpoint. Under stationary settings, the inference is simple in the sense…