Publications

Last updated on Aug 26, 2025

Working Papers

• Wavelet Factor model 

• Prediction Birth Weight by Multivariate Functional Principal Component Regressions - submitted

• PM10 prediction in South Korea via ensemble tree-based methods 

• Extreme Quantile Estimation via functional Single Index Models and Generalized Pareto Tail Approximation - submitted

Selected Publications

• Y. Lim and Y Park, (2025). Enhancing Air Quality Forecasting with Functional Neural Networks: A Case Study of PM2.5 in Seoul, Atmospheric Pollution Research, Accepted for publication.   

• Y. Lim, Lu, R., Ville, M. S., and Chen, Z.(2025). Bayesian additive tree ensembles for composite quantile regressions. Statistics and Computing. 35, 175. 

• Park, S., Y. Lim, and H-S Oh (2025). Combined quantile forecasting for high-dimensional non-Gaussian data. Environmetrics. 36(6) 

• TY Kim, H-S Oh, and Y. Lim∗  (2025). Forecasting High-Dimensional Non-Normal Time Series Using Averaged Quantile Regression. Journal of Time Series Econometrics. https://doi.org/10.1515/jtse-2024-0014 

• TH Kim and Y. Lim∗ (2024). Environmental Spillovers and Local Agglomeration: Evidence from Large Plant Openings.  Journal of Behavioral and Experimental Finance, 43, 100968.

•  Y Park, H-S Oh, and Y. Lim∗ (2024). A data-adaptive dimension reduction for functional data via penalized low-rank approximation, Statistics and Computing, 34, 36.

• WK Song, H-S Oh, Cheung, Y. K., and Y. Lim∗ (2024). Multi-feature Clustering of Step Data using Multivariate Functional Principal Component Analysis, Statistical Papers, 65(4), 2109-2134.

•  MJ Kim, H-S Oh, and Y. Lim∗ (2023). Zero-inflated time-series clustering via ensemble thick-pen transform, Journal of Classification, 40(2), 407-431.

• Y Park, HS Kim, and Y. Lim∗ (2023). Functional principal component analysis for partially observed elliptical process, Computational Statistics & Data analysis, 84, 107745.

• HS Kim and Y. Lim∗(2023). Functional clustering on a sphere via Riemannian functional principal components. Stat, e557.

• Y. Lim and H-S. Oh (2022). Quantile Spectral Analysis of Long-Memory Processes. Empirical Economics, 62, 1245-1266.

• H Kim and Y. Lim∗ (2022). Bootstrap aggregated classification for sparse functional data. Journal of Applied Statistics, 49(8), 2052-2063.

• S. Kang and Y. Lim∗ (2021). Ensemble Mapper. Stat. 10(1), e405.

• Y. Lim, Kwon, J. and H-S. Oh (2021). Principal Component Analysis in the Wavelet Domain. Pattern recognition. 108096.

• Kim, J., Park, S., Kwon, J., Y. Lim, and H-S. Oh (2021). Estimation of spatio-temporal extreme distribution using a quantile factor model. Extremes, 24(1), 177-195.

• Jang, J. Y., H-S. Oh, Y. Lim, and Cheung, Y. K. (2021). Ensemble clustering for step data via binning. Biometrics, 77(1), 293–304.

• Y. Lim and H-S. Oh (2021). Robust Coherence Analysis for Long-Memory Processes. Applied Economics Letters, 28(5), 335–342.

• Y. Lim, H-S. Oh and Y. Kuen (2020). A generalization of functional clustering for discrete multivariate longitudinal data. Statistical methods in medical research, 29(11), 3205–3217.

• Y. Lim (2020). GEE approach to estimating accuracy and its confidence intervals for correlated data. Pharmaceutical Statistics. 19(1), 59–70.

• J-H Kwon, H-S. Oh and Y. Lim∗ (2020). Dynamic Principal Component Analysis with Missing Value. Journal of Applied Statistics, 47(11), 1957–1969. 2019

• Y. Lim, H-S. Oh and Y. Kuen (2019). Multiscale Clustering for Functional Data. Journal of Classification. 36(2). 368–391.

• Y. Lim, H-S. Oh and Y. Kuen (2019). Functional Clustering of Accelerometer Data via Transformed Input Variables. Journal of the Royal Statistical Society C, 68(3), 495–520.

• Y. Lim, Choi, J. S., et. al (2019). Comparative analysis of diagnostic procedures for tumor detection rates in paired data. Statistical methods in medical research, 28(5), 1477–1488.

• Y. Lim and Eun Sook Ko, et.al (2018) Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer. Clinical Cancer Research, pp.clincanres-3783. 

• J-P Kim, H-S. Oh, and Y. Lim∗ (2017). Seasonal Precipitation Prediction via Data-Adaptive Principal Component Regression. International Journal of Climatology, 37(S1), 75-86.

• Y. Lim and H-S. Oh (2016). Composite quantile periodogram for spectral analysis. Journal of Time Series Analysis, 37, 195–221.

• Y. Lim and H-S. Oh (2016). A Data-Adaptive Principal Component Analysis: Use of Composite Asymmetric Huber Function. Journal of Computational and Graphical Statistics, 25(4), 1230-1247.