Zobrazeno 1 - 10
of 297
pro vyhledávání: '"Oh, Hee-Seok"'
Autor:
Shin, Ha-Young, Oh, Hee-Seok
Several new geometric quantile-based measures for multivariate dispersion, skewness, kurtosis, and spherical asymmetry are defined. These measures differ from existing measures, which use volumes and are easy to calculate. Some theoretical justificat
Externí odkaz:
http://arxiv.org/abs/2407.07297
We define the notions of absolute average and median treatment effects as causal estimands on general metric spaces such as Riemannian manifolds, propose estimators using stratification, and prove several properties, including strong consistency. In
Externí odkaz:
http://arxiv.org/abs/2407.03726
Autor:
Kang, Seungwoo, Oh, Hee-Seok
A new measure to assess the centrality of vertices in an undirected and connected graph is proposed. The proposed measure, L1 centrality, can adequately handle graphs with weights assigned to vertices and edges. The study provides tools for graphical
Externí odkaz:
http://arxiv.org/abs/2404.13233
This study proposes a novel method for forecasting a scalar variable based on high-dimensional predictors that is applicable to various data distributions. In the literature, one of the popular approaches for forecasting with many predictors is to us
Externí odkaz:
http://arxiv.org/abs/2402.17297
Autor:
Shin, Ha-Young, Oh, Hee-Seok
This paper develops a notion of geometric quantiles on Hadamard spaces, also known as global non-positive curvature spaces. After providing some definitions and basic properties, including scaled isometry equivariance and a necessary condition on the
Externí odkaz:
http://arxiv.org/abs/2312.10870
We propose a computationally and statistically efficient procedure for segmenting univariate data under piecewise linearity. The proposed moving sum (MOSUM) methodology detects multiple change points where the underlying signal undergoes discontinuou
Externí odkaz:
http://arxiv.org/abs/2208.04900
Autor:
Kim, Kyusoon, Oh, Hee-Seok
Publikováno v:
In International Journal of Forecasting July-September 2024 40(3):971-984
Autor:
Choi, Guebin, Oh, Hee-Seok
Publikováno v:
In Pattern Recognition Letters June 2024 182:76-82
Autor:
Kang, Seungwoo, Oh, Hee-Seok
Publikováno v:
In International Journal of Forecasting January-March 2024 40(1):124-141
The estimation of functions with varying degrees of smoothness is a challenging problem in the nonparametric function estimation. In this paper, we propose the LABS (L\'{e}vy Adaptive B-Spline regression) model, an extension of the LARK models, for t
Externí odkaz:
http://arxiv.org/abs/2101.12179