Picture of Hyejin Shin

Hyejin Shin

Seoul, South Korea
Member of Technical Staff

Education

2006 Ph.D., Statistics, Texas A&M University, College Station, Texas, USA

Biography

Hyejin Shin has been working at Bell Labs Seoul as a member of technical staff since January 2013. Before joining Bell Labs Seoul, she worked as an assistant professor in the department of Mathematics and Statistics at Auburn University from 2006 to 2010 and an associate research professor in the department of Statistics at Seoul National University from 2011 to 2012.

Professional Activities

Refereeing activities for Journal of Multivariate Analysis; Annals of Statistics; Statistica Sinica; Statistics and Probability Letters; Applied Mathematical Modelling; Journal of Nonparametric Statistics; Journal of Korean Statistical Society


Selected Articles and Publications

  • Shin, H. (2008). An extension of Fisher's discriminant analysis for stochastic processes. Journal of Multivariate Analysis, 99, 1191-1216.
  • Shin, H. (2009). Partial functional linear regression. Journal of Statistical Planning and Inference, 139, 3405-3418.
  • Kupresanin, A., Shin, H., King, D., Eubank, R. L. (2010). An RKHS framework for functional data analysis. Journal of Statistical Planning and Inference, 140, 3627-3637.
  • Shin, H., Eubank, R. L. (2011). Unit canonical correlations and high-dimensional discriminant analysis. Journal of Statistical Computation and Simulation, 81, 167-178.
  • Son, A., Schmidt, C. J., Shin, H., Cha, D. K. (2011). Microbial community analysis of perchlorate-reducing cultures growing on zero-valent iron. Journal of Hazardous Materials, 185, 669-676.
  • Sawant, P., Billor, N., Shin, H. (2012). Functional outlier detection with robust functional principal component analysis. Computational Statistics, 27, 83-102.
  • Shin, H., Lee, M. H. (2012). On prediction rate in partial functional linear regression. Journal of Multivariate Analysis, 103, 93-106.
  • Shin, H., Hsing, T. (2012). Linear prediction in functional data analysis. Stochastic Processes and their Applications, 122, 3680-3700.
  • Lee, S., Shin, H., Billor, N. (2013). M-type smoothing spline estimators for principal functions. Computational Statistics and Data Analysis, 66, 89-100.