오희석 교수

We propose a robust curve and surface estimator based on <it>M</it>-type estimators and penalty-based smoothing. This approach also includes an application to wavelet regression. The concept of pseudo data, a transformation of the robust additive model to the one with bounded errors, is used to derive some theoretical properties and also motivate a computational algorithm. The resulting algorithm, termed the es-algorithm, is computationally fast and provides a simple way of choosing

Summary The objective is to estimate the period and the light curve (or periodic function) of a variable star. Previously, several methods have been proposed to estimate the period of a variable star, but they are inaccurate especially when a data set contains outliers. We use a smoothing spline regression to estimate the light curve given a period and then find the period which minimizes the generalized cross-validation (GCV). The GCV method works well, matching an intensive visual examination

Abstract 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 EMD. The proposed algorithm, called statistical EMD (SEMD), uses a smoothing technique instead of an interpolation when constructing upper and lower envelopes. Using SEMD, we discuss how to identify non-informative fluctuations such as noise, outliers, and ultra-high frequency com

The calculation of nonparametric quantile regression curve estimates is often computationally intensive, as typically an expensive nonlinear optimization problem is involved. This article proposes a fast and easy-to-implement method for computing such estimates. The main idea is to approximate the costly nonlinear optimization by a sequence of well-studied penalized least squares-type nonparametric mean regression estimation problems. The new method can be paired with different nonparametric smo

Journal Article Polynomial boundary treatment for wavelet regression Get access Hee‐Seok Oh, Hee‐Seok Oh Search for other works by this author on: Oxford Academic Google Scholar Philippe Naveau, Philippe Naveau Search for other works by this author on: Oxford Academic Google Scholar Geunghee Lee Geunghee Lee Search for other works by this author on: Oxford Academic Google Scholar Biometrika, Volume 88, Issue 1, 1 February 2001, Pages 291–298, https://doi.org/10.1093/biomet/88.1.291 Published: 01

Summary The paper considers the problem of estimating the entire temperature field for every location on the globe from scattered surface air temperatures observed by a network of weather-stations. Classical methods such as spherical harmonics and spherical smoothing splines are not efficient in representing data that have inherent multiscale structures. The paper presents an estimation method that can adapt to the multiscale characteristics of the data. The method is based on a spherical wavele

Abstract This article proposes a statistical method based on the regularized canonical correlation analysis (RCCA) to improve on the conventional canonical correlation analysis (CCA) method for seasonal climate prediction. The fundamental idea of this method is to combine the regularization principle with the classical CCA to handle high‐dimensional data in which the number of variables is larger than the number of observations. This study focuses on prediction of future precipitation for the bo

Abstract 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 introduce a new approach, termed “ensemble patch transformation” that provides a framework for decomposition and filtering of signals; thus, as a result, it enhances identification of local characteristics embedded in a signal that is crucial for signal decomposition and designs flexib