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Article
Multi-feature clustering of step data using multivariate functional principal component analysis
This study presents a new statistical method for clustering step data, a popular form of health recording data easily obtained from wearable devices. As step data are high-dimensional and zero-inflated, classi...
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Article
Novel sampling method for the von Mises–Fisher distribution
The von Mises–Fisher distribution is a widely used probability model in directional statistics. An algorithm for generating pseudo-random vectors from this distribution was suggested by Wood (Commun Stat Simul...
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Article
A data-adaptive dimension reduction for functional data via penalized low-rank approximation
We introduce a data-adaptive nonparametric dimension reduction tool to obtain a low-dimensional approximation of functional data contaminated by erratic measurement errors following symmetric or asymmetric dis...
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Article
Open AccessZero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform
This study develops a new clustering method for high-dimensional zero-inflated time series data. The proposed method is based on thick-pen transform (TPT), in which the basic idea is to draw along the data wit...
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Article
Quantile spectral analysis of long-memory processes
This study examines the problem of robust spectral analysis of long-memory processes. We investigate the possibility of using Laplace and quantile periodograms for a non-Gaussian distribution structure. The La...
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Article
Robust Geodesic Regression
This paper studies robust regression for data on Riemannian manifolds. Geodesic regression is the generalization of linear regression to a setting with a manifold-valued dependent variable and one or more real...
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Article
Special Issue on the 50th Anniversary of the Korean Statistical Society
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Article
Estimation of spatio-temporal extreme distribution using a quantile factor model
This paper describes the estimation of the extreme spatio-temporal sea surface temperature data based on the quantile factor model implemented by the SNU multiscale team. The proposed method was developed for ...
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Article
Open AccessEnsemble patch transformation: a flexible framework for decomposition and filtering of signal
This paper considers the problem of signal decomposition and filtering by extending its scope to various signals that cannot be effectively dealt with existing methods. For the core of our methodology, we intr...
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Article
Multiscale Clustering for Functional Data
In an era of massive and complex data, clustering is one of the most important procedures for understanding and analyzing unstructured multivariate data. Classical methods such as K-means and hierarchical cluster...
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Article
Prediction of extremal precipitation by quantile regression forests: from SNU Multiscale Team
This paper considers the problem of spatio-temporal extreme value prediction of precipitation data. This work presents some methods that predict monthly extremes over the next 20 years corresponding to 0.998 q...
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Article
Spatio-temporal analysis of particulate matter extremes in Seoul: use of multiscale approach
This paper considers a problem of analyzing temporal and spatial structure of particulate matter (PM) data with emphasizing high-level ...
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Article
Identifying local smoothness for spatially inhomogeneous functions
We consider a problem of estimating local smoothness of a spatially inhomogeneous function from noisy data under the framework of smoothing splines. Most existing studies related to this problem deal with esti...
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Article
Open AccessEmpirical mode decomposition with missing values
This paper considers an improvement of empirical mode decomposition (EMD) in the presence of missing data. EMD has been widely used to decompose nonlinear and nonstationary signals into some components accord...
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Article
Simultaneous confidence interval for quantile regression
This paper considers a problem of constructing simultaneous confidence intervals for quantile regression. Recently, Krivobokova et al. (J Am Stat Assoc 105:852–863, 2010) provided simultaneous confidence interval...
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Article
Independent component regression for seasonal climate prediction: an efficient way to improve multimodel ensembles
The main goal of this study is to improve the seasonal climate prediction of multimodel ensembles. The conventional principal component regression has been used to build a statistical relation between observat...
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Article
Variable selection in quantile regression when the models have autoregressive errors
This paper considers a problem of variable selection in quantile regression with autoregressive errors. Recently, Wu and Liu (2009) investigated the oracle properties of the SCAD and adaptive-LASSO penalized q...
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Article
Robust principal component analysis via ES-algorithm
In this paper, a new method for robust principal component analysis (PCA) is proposed. PCA is a widely used tool for dimension reduction without substantial loss of information. However, the classical PCA is v...
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Article
The role of functional data analysis for instantaneous frequency estimation
This paper proposes a method for estimating the instantaneous frequency of a nonstationary signal; this method is based on a combination of empirical mode decomposition and functional data analysis. The propos...
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Article
Open AccessExtending the scope of empirical mode decomposition by smoothing
This article considers extending the scope of the empirical mode decomposition (EMD) method. The extension is aimed at noisy data and irregularly spaced data, which is necessary for widespread applicability of...
