Wavelet spectrum and its characterization property for random processes. - Ta-Hsin Li; Hee-Seok Oh (IEEE Trans. Inf. Theory, 2002)

Automatic polynomial wavelet regression. - Thomas C. M. Lee; Hee-Seok Oh (Stat. Comput., 2004)

Polynomial wavelet regression for images with irregular boundaries. - Philippe Naveau; Hee-Seok Oh (IEEE Trans. Image Process., 2004)

Hybrid local polynomial wavelet shrinkage: wavelet regression with automatic boundary adjustment. - Hee-Seok Oh; Thomas C. M. Lee (Comput. Stat. Data Anal., 2005)

Hierarchical-likelihood-based wavelet method for denoising signals with missing data. - Donghoh Kim; Youngjo Lee 001; Hee-Seok Oh (IEEE Signal Process. Lett., 2006)

Robust penalized regression spline fitting with application to additive mixed modeling. - Thomas C. M. Lee; Hee-Seok Oh (Comput. Stat., 2007)

Bayesian selection of primary resolution and wavelet basis functions for wavelet regression. - Chun Gun Park; Hee-Seok Oh; Hakbae Lee (Comput. Stat., 2008)

Reliability functions estimated from commonly used yield models. - Kyungmee O. Kim; Hee-Seok Oh (Microelectron. Reliab., 2008)

Cross-validated wavelet shrinkage. - Hee-Seok Oh; Donghoh Kim; Youngjo Lee 001 (Comput. Stat., 2009)

EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum. - Donghoh Kim; Hee-Seok Oh (R J., 2009)

Robust wavelet shrinkage using robust selection of thresholds. - Hee-Seok Oh; Donghoh Kim; Yongdai Kim (Stat. Comput., 2009)

A Reinterpretation of EMD by cubic spline interpolation. - Minjeong Park; Donghoh Kim; Hee-Seok Oh (Adv. Data Sci. Adapt. Anal., 2011)

Enhancement of spatially adaptive smoothing splines via parameterization of smoothing parameters. - Dongik Jang; Hee-Seok Oh (Comput. Stat. Data Anal., 2011)

Extending the Scope of Empirical Mode Decomposition by Smoothing. - Donghoh Kim; Kyungmee O. Kim; Hee-Seok Oh (EURASIP J. Adv. Signal Process., 2012)

Bidimensional Statistical Empirical Mode Decomposition. - Donghoh Kim; Minjeong Park; Hee-Seok Oh (IEEE Signal Process. Lett., 2012)

The role of functional data analysis for instantaneous frequency estimation. - Minjeong Park; Sinsup Cho; Hee-Seok Oh (Comput. Stat., 2013)

Bayesian regression based on principal components for high-dimensional data. - Jaeyong Lee; Hee-Seok Oh (J. Multivar. Anal., 2013)

A new sparse variable selection via random-effect model. - Youngjo Lee 001; Hee-Seok Oh (J. Multivar. Anal., 2014)

Simultaneous confidence interval for quantile regression. - Yaeji Lim; Hee-Seok Oh (Comput. Stat., 2015)

Quantile-Based Empirical Mode Decomposition: An Efficient Way to Decompose Noisy Signals. - Minsu Park; Donghoh Kim; Hee-Seok Oh (IEEE Trans. Instrum. Meas., 2015)

Intrinsic Pattern Preserving Boundary Treatment Method for Empirical Mode Decomposition. - Donghoh Kim; Hee-Seok Oh (Adv. Data Sci. Adapt. Anal., 2016)

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios. - Yunfei Chen 001; Hee-Seok Oh (IEEE Commun. Surv. Tutorials, 2016)

Spectrum measurement modelling and prediction based on wavelets. - Yunfei Chen 001; Hee-Seok Oh (IET Commun., 2016)

Identifying local smoothness for spatially inhomogeneous functions. - Dongik Jang; Hee-Seok Oh; Philippe Naveau (Comput. Stat., 2017)

Enhancement of variational mode decomposition with missing values. - Guebin Choi; Hee-Seok Oh; Donghoh Kim (Signal Process., 2018)

Multiscale Clustering for Functional Data. - Yaeji Lim; Hee-Seok Oh; Ying-Kuen K. Cheung (J. Classif., 2019)

Robust Multivariate Regression on Riemannian Manifolds. - Sangyul Lee; Hee-Seok Oh (DSAA, 2020)

Ensemble patch transformation: a flexible framework for decomposition and filtering of signal. - Donghoh Kim; Guebin Choi; Hee-Seok Oh (EURASIP J. Adv. Signal Process., 2020)

Pseudo-quantile functional data clustering. - Joonpyo Kim; Hee-Seok Oh (J. Multivar. Anal., 2020)

Image decomposition by bidimensional ensemble patch transform. - Hee-Seok Oh; Donghoh Kim (Pattern Recognit. Lett., 2020)

Spherical Principal Curves. - Jongmin Lee; Jang-Hyun Kim; Hee-Seok Oh (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Principal component analysis in the wavelet domain. - Yaeji Lim; Junhyeon Kwon; Hee-Seok Oh (Pattern Recognit., 2021)

EPT: An R package for ensemble patch transform. - Donghoh Kim; Hee-Seok Oh; Guebin Choi (SoftwareX, 2021)

Robust Geodesic Regression. - Ha Young Shin; Hee-Seok Oh (Int. J. Comput. Vis., 2022)

spherepc: An R Package for Dimension Reduction on a Sphere. - Jongmin Lee; Jang-Hyun Kim; Hee-Seok Oh (R J., 2022)

Decomp2d: An R solution for image decomposition. - Hee-Seok Oh; Guebin Choi; Donghoh Kim (SoftwareX, 2022)

Elastic-band transform for visualization and detection. - Guebin Choi; Hee-Seok Oh (Pattern Recognit. Lett., 2023)

Robust spherical principal curves. - Jongmin Lee; Hee-Seok Oh (Pattern Recognit., 2023)

Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform. - Minji Kim; Hee-Seok Oh; Yaeji Lim (J. Classif., 2023)

Probabilistic Principal Curves on Riemannian Manifolds. - Seungwoo Kang; Hee-Seok Oh (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Decomposition via elastic-band transform. - Guebin Choi; Hee-Seok Oh (Pattern Recognit. Lett., 2024)

GSD: An R package for graph signal decomposition. - Hyeonglae Cho; Hee-Seok Oh; Donghoh Kim (SoftwareX, 2024)

A data-adaptive dimension reduction for functional data via penalized low-rank approximation. - Yeonjoo Park; Hee-Seok Oh; Yaeji Lim (Stat. Comput., 2024)

Novel sampling method for the von Mises-Fisher distribution. - Seungwoo Kang; Hee-Seok Oh (Stat. Comput., 2024)

Moving Sum Procedure for Change Point Detection under Piecewise Linearity. - Joonpyo Kim; Hee-Seok Oh; Haeran Cho (Technometrics, 2024)

Prediction of Wafer Performance: Use of Functional Outlier Detection and Regression. - Kyusoon Kim; Seunghee Oh; Kiwook Bae; Hee-Seok Oh (IEEE Access, 2025)

RSFclust: Robust sparse clustering of functional data using quantile curves. - Chihoon Lee; Hee-Seok Oh; Joonpyo Kim (Stat. Comput., 2025)