Author: Chen, Kun

Book on reduced-rank regression is published by Springer

Chen’s co-authored book on reduced-rank regression has been published by Springer.

Reinsel, G. C., Velu, R. P., and Chen, K. (2022) Multivariate Reduced-Rank Regression: Theory, Methods and Applications, 2nd Edition. Springer.

The book’s first edition has profoundly influenced Chen’s research since his graduate study at the University of Iowa under the supervision of Professor Kung-Sik Chan. Chen is honored and humbled to be a co-author in this new edition. 

 

This book provides an account of multivariate reduced-rank regression, a tool of multivariate analysis that enjoys a broad array of applications. In addition to a historical review of the topic, its connection to other widely used statistical methods, such as multivariate analysis of variance (MANOVA), discriminant analysis, principal components, canonical correlation analysis, and errors-in-variables models, is also discussed.

This new edition incorporates Big Data methodology and its applications, as well as high-dimensional reduced-rank regression, generalized reduced-rank regression with complex data, and sparse and low-rank regression methods. Each chapter contains developments of basic theoretical results, as well as details on computational procedures, illustrated with numerical examples drawn from disciplines such as biochemistry, genetics, marketing, and finance.

This book is designed for advanced students, practitioners, and researchers, who may deal with moderate and high-dimensional multivariate data. Because regression is one of the most popular statistical methods, the multivariate regression analysis tools described should provide a natural way of looking at large (both cross-sectional and chronological) data sets. This book can be assigned in seminar-type courses taken by advanced graduate students in statistics, machine learning, econometrics, business, and engineering.

Project on integrative learning of quantum dots is funded by NSF

Our project on “Integrative Learning of Fluorescence Fluctuations in Perovskite Quantum Dots Using A Data Science Assisted Single-Particle Approach” has been funded by NSF. This is a collaboration between Dr. Jing Zhao (PI) from UConn Chemistry, Dr. Chen (Co-PI) from UConn Stat, and Dr. Ou Chen (Co-PI) from Brown Chemistry. Our long-term goal is to integrate materials science, single-particle spectroscopy, and modern data science approaches to develop and characterize QDs for desired applications.

Our work on fusion-based suicide prediction is featured in JAMIA

Our work on using fusion learning t0 build suicide risk prediction models for diverse clinical settings (link) is featured in the most recent issue of the Journal of the American Medical Informatics Association (JAMIA).

Here is the Editorial by JAMIA editor Suzanne Bakken: Addressing Consequential Public Health Problems Through Informatics and Data Science.

Congratulations to the team!

Biju Wang defended his thesis. Congratulations, Dr. Wang!

On July 22, Ph.D. student Biju Wang successfully defended his thesis entitled “On Targeted Integrative Learning via Distance Segmented Regression”. The defense took place online via WebEx and was attended by many students and faculty members. Biju’s committee consists of Drs. Kun Chen, James Grady, Yuwen Gu, Yang Song, and Jun Yan. Thank you all for your support!

After graduation, Biju will join Johnson & Johnson.

Congratulations, Dr. Wang!

Wanwan Xu defended her thesis. Congratulations, Dr. Xu!

On June 24, Ph.D. student Wanwan Xu successfully defended her thesis entitled “Topics on Statistical Data Fusion with Public Health Applications”. The defense took place online via WebEx and was attended by many students and faculty members. Wanwan’s committee consists of Drs. Robert Aseltine, Kun Chen, Yuwen Gu, and Jun Yan. Thank you all for your support!

After graduation, Wanwan will be a Post-Doc at the Department of Biostatistics at Yale University.

Congratulations, Dr. Xu!

Yan Li defended his thesis. Congratulations, Dr. Li!

On June 23, Ph.D. student Yan Li successfully defended his thesis entitled “Amalgamation-Based Statistical Learning for Compositional Data”. The defense took place online via WebEx and was attended by many students and faculty members. Yan is co-advised by Drs. Kun Chen and Jun Yan, and his committee consists of Drs. Robert Aseltine, Kun Chen, Elizabeth Schifano, and Jun Yan. Thank you all for your support.

After graduation, Yan will be a Post-Doc at the Department of Biostatistics at the University of Michigan.

Congratulations, Dr. Li!