Seung Jun Shin

Works (3)

Updated: July 5th, 2023 15:49

2018 article

Principal quantile regression for sufficient dimension reduction with heteroscedasticity

Wang, C., Shin, S. J., & Wu, Y. (2018, January 1). Electronic Journal of Statistics.

By: C. Wang n, S. Shin* & Y. Wu*

author keywords: Heteroscedasticity; kernel quantile regression; principal quantile regression; sufficient dimension reduction
topics (OpenAlex): Statistical Methods and Inference; Advanced Statistical Methods and Models; Control Systems and Identification
TL;DR: A new SDR method called principal quantile regression (PQR) is proposed that efficiently and competitively tackles heteroscedasticity and can naturally be extended to a nonlinear version via kernel trick. (via Semantic Scholar)
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Source: Web Of Science
Added: March 25, 2019

2013 article

Two-Dimensional Solution Surface for Weighted Support Vector Machines

Shin, S. J., Wu, Y., & Zhang, H. H. (2013, January 23). Journal of Computational and Graphical Statistics.

By: S. Shin n, Y. Wu n & H. Zhang*

topics (OpenAlex): Face and Expression Recognition; Machine Learning and ELM; Machine Learning and Data Classification
TL;DR: This article establishes that the WSVM solutions are jointly piecewise-linear with respect to both the regularization and weight parameter and develops a state-of-the-art algorithm that can compute the entire trajectory of theWSVM solutions for every pair of the regularized parameter and the weight parameter at a feasible computational cost. (via Semantic Scholar)
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Source: NC State University Libraries
Added: August 6, 2018

2009 article

Bootstrapping Spatial Median for Location Problems

Jhun, M., & Shin, S. J. (2009, September 29). Communications in Statistics - Simulation and Computation.

By: M. Jhun & S. Shin*

author keywords: Bootstrap; Multivariate location; Simultaneous confidence interval; Spatial median
topics (OpenAlex): Advanced Statistical Methods and Models; Advanced Statistical Process Monitoring; Statistical Methods and Inference
TL;DR: The results show that bootstrap method is efficient for the estimation of the covariance matrix of the sample spatial median and proposed bootstrap simultaneous confidence intervals based on the spatial median for multiple comparisons in the multi-sample case. (via Semantic Scholar)
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Source: Web Of Science
Added: August 6, 2018

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