3 citations · 6 across the 8 of their papers we have counts for
6 papers · 1 filter
Identification problem and quasi-maximum likelihood estimation for matrix-variate CP-factor models
Hanzi Ye, Chun Yip Yau
Matrix-valued time series, arising in diverse fields such as economics, neuroscience, and recommender systems, have become increasingly prominent in modern data analysis. Among var…
Inference for Multiple Change-points in Piecewise Locally Stationary Time Series
Wai Leong Ng, Xinyi Tang, Mun Lau Cheung +3
Change-point detection and locally stationary time series modeling are two major approaches for the analysis of non-stationary data. The former aims to identify stationary phases b…
Predicting Future Change-points in Time Series
Chak Fung Choi, Chunxue Li, Chun Yip Yau +1
Change-point detection and estimation procedures have been widely developed in the literature. However, commonly used approaches in change-point analysis have mainly been focusing…
Functional Autoregressive Processes in Reproducing Kernel Hilbert Spaces
Daren Wang, Zifeng Zhao, Rebecca Willett +1
We study the estimation and prediction of functional autoregressive~(FAR) processes, a statistical tool for modeling functional time series data. Due to the infinite-dimensional na…
Alternating Pruned Dynamic Programming for Multiple Epidemic Change-Point Estimation
Zifeng Zhao, Chun Yip Yau
In this paper, we study the problem of multiple change-point detection for a univariate sequence under the epidemic setting, where the behavior of the sequence alternates between a…
A Composite Likelihood-based Approach for Change-point Detection in Spatio-temporal Processes
Zifeng Zhao, Ting Fung Ma, Wai Leong Ng +1
This paper develops a unified and computationally efficient method for change-point estimation along the time dimension in a non-stationary spatio-temporal process. By modeling a n…