Kyungduk Ko is an Associate Professor in the Department of Mathematics. Dr. Ko's research focuses on the theory and practice of long memory processes and on the development of wavelet-based statistical models and their application. In addition to teaching courses in statistics and supervising graduate student research, Dr. Ko has served on a variety of campus committees. He has also served a reviewer for publications such as: Statistica Sinica, The Canadian Journal of Statistics, Communications in Statistics – Simulation and Computation, and the Journal of Applied Probability and Statistics.
Articles
First-Order Bias Correction for Fractionally Integrated Time Series (with Jaechoul Lee), Canadian Journal of Statistics (2009)
Confidence Intervals for Long Memory Regressions (with Jaechoul Lee and Robert Lund), Statistics & Probability Letters (2008)
This paper proposes an accurate con dence interval for the trend parameter in a linear...
Bayesian Wavelet-Based Methods for the Detection of Multiple Changes of the Long Memory Parameter, IEEE Transactions on Signal Processing (2006)
Long memory processes are widely used in many scientific fields, such as economics, physics, and...
Wavelet Deconvolution in a Periodic Setting Using Cross-Validation (with Leming Qu and Partha S. Routh), IEEE Signal Processing Letters (2006)
The wavelet deconvolution method WaveD using band-limited wavelets offers both theoretical and computational advantages over...