Office: MB 222-A
(208) 426-1123
kyungdukko@boisestate.edu
About
Kyungduk Ko’s research focuses on the theory and practice of Bayesian inference, long memory processes, and wavelet-based statistical models with applications to financial data and climatory data. Work on Bayesian inference and wavelet transformations was on parameter estimations and change point detection (JSPI 2006, IEEE Trans. Sig. Proc. 2006). Contributions on statistical modelings on long memory data include partial linear regression models (Sinica 2009), power transformation (ASMBI 2009), bias correction (CJS 2009), and wavelet-based regression models (Biometrics 2013). Recent research interests lie in the area of change point analysis and inference on correlated or/and non-symetric data.
Statistics at Boise State University
Selected courses taught
- MATH 254 Introduction to Statistics
- MATH 360 Engineering Statistics
- MATH 361 Probability & Statistics I
- MATH 462/562 Probability & Statistics II
- MATH 471/571 Data Analysis