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
A Wavelet-Based Bayesian Approach to Regression Models with Long Memory Errors and Its Application to fMRI Data (with Jaesik Jeong and Marina Vannucci), Biometrics (2013)
This article considers linear regression models with long memory errors. These models have been proven...
Inducing Normality from Non-Gaussian Long Memory Time Series and Its Application to Stock Return Data, Applied Stochastic Models in Business and Industry (2010)
Motivated by Lee and Ko (Appl. Stochastic Models. Bus. Ind. 2007; 23:493–502) but not limited...
Wavelet-based Bayesian Estimation of Partially Linear Regression Models with Long Memory Errors (with Leming Qu and Marina Vannucci), Statistica Sinica (2009)
In this paper we focus on partially linear regression models with long memory errors, and...
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...