4 papers
On a fast consistent selection of nested models with possibly unnormalized probability densities
Rong Bian, Kung-Sik Chan, Bing Cheng +1
Models with unnormalized probability density functions are ubiquitous in statistics, artificial intelligence and many other fields. However, they face significant challenges in mod…
Mixture Matrix-valued Autoregressive Model
Fei Wu, Kung-Sik Chan
Time series of matrix-valued data are increasingly available in various areas including economics, finance, social science, among others. These data may shed light on the inter-dyn…
Adaptive Change Point Detection with Matrix Time Series: Leveraging Structured Mean Shifts
Xinyu Zhang, Kung-Sik Chan
In high-dimensional time series, the component processes are often assembled into a matrix to display their interrelationship. We focus on detecting mean shifts with unknown change…
Spectral Change Point Estimation for High Dimensional Time Series by Sparse Tensor Decomposition
Xinyu Zhang, Kung-Sik Chan
Multivariate time series may be subject to partial structural changes over certain frequency band, for instance, in neuroscience. We study the change point detection problem with h…