99 citations
- University of IowaUS6 papers
- East China Normal UniversityCN1 paper
- Institute of Statistical Science, Academia SinicaTW1 paper
- Program Support CenterUS1 paper
- Purdue University West LafayetteUS1 paper
- The Coordinating CenterUS1 paper
- University of ChicagoUS1 paper
- University of Chinese Academy of SciencesCN1 paper
- University of Illinois ChicagoUS1 paper
- Yale UniversityUS1 paper
6 papers
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…
An Asynchronous Distributed Expectation Maximization Algorithm For Massive Data: The DEM Algorithm
Sanvesh Srivastava, Glen DePalma, Chuanhai Liu
The family of Expectation-Maximization (EM) algorithms provides a general approach to fitting flexible models for large and complex data. The expectation (E) step of EM-type algori…
Promoting Similarity of Sparsity Structures in Integrative Analysis with Penalization
Yuan Huang, Qingzhao Zhang, Sanguo Zhang +2
For data with high-dimensional covariates but small to moderate sample sizes, the analysis of single datasets often generates unsatisfactory results. The integrative analysis of mu…
Inference of seasonal long-memory aggregate time series
Kung-Sik Chan, Henghsiu Tsai
Time-series data with regular and/or seasonal long-memory are often aggregated before analysis. Often, the aggregation scale is large enough to remove any short-memory components o…
Inference of time-varying regression models
Ting Zhang, Wei Biao Wu
We consider parameter estimation, hypothesis testing and variable selection for partially time-varying coefficient models. Our asymptotic theory has the useful feature that it can…
Reading policies for joins: An asymptotic analysis
Ralph P. Russo, Nariankadu D. Shyamalkumar
Suppose that observations are made from the distribution and from the distribution . Associate with each pair, from and $…