The statistics group at Boise State currently consists of Kyungduk Ko and Fan Zhang.
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.
Fan Zhang‘s research focuses on design and analysis of experiments, Bayesian variable selection, uncertainty quantification, and Bayesian modeling and inference.