Leming Qu’s research interests are Scientific Computing, Nonparametric and Semiparametric Estimation, Statistical and Computational Inverse Problems, Statistical Signal Processing, Machine Learning, Bayesian Modeling. Leming Qu is also intrerested in Statistical Consulting.
Jaechoul Lee’s research interests are time series analysis and its application to real-world problems occurring in climatology and finance. His most recent research work includes a study on the efficiency of least squares estimators in time series regression, a simple analysis of long memory time series data, and a statical assessment of trends in climatic data through periodic time series models.
Kyungduk 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. Early work on long memory processes was on the parameter estimation and change point detection of ARFIMA processes (JSPI 2006, TSP 2006). Contributions on statistical modeling with long memory processes include partial linear regression models (Sinica 2008), ANOVA (ASMBI 2007) and linear trend regression models (SPL 2008) with long memory errors. His research is mainly based on wavelet transforms and Bayesian inference with application to financial time series, climate time series and functional magnetic resonance imaging (fMRI) data.