Zobrazeno 1 - 10 of 26 pro vyhledávání: '"Sungwan Bang"'
2
Model-based recursive partitioning algorithm to penalized non-crossing multiple quantile regression for the right-censored data
Autor: Byoungwook Jang, Jaeoh Kim, Sungwan Bang
Publikováno v: Communications in Statistics - Simulation and Computation. :1-17
Quantile functions of the response variable provide a tool for practitioners to analyze both the central tendency and statistical dispersion of data. As a counterpart to the regression tree models,...
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ed3eaa00227be901b7e93fb077cd5165
https://doi.org/10.1080/03610918.2021.1944643
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4
Unified Noncrossing Multiple Quantile Regressions Tree
Autor: Jaeoh Kim, HyungJun Cho, Sungwan Bang
Publikováno v: Journal of Computational and Graphical Statistics. 28:454-465
In this article, we consider the estimation problem of a tree model for multiple conditional quantile functions of the response. Using the generalized, unbiased interaction detection and estimation...
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1be47a6a2e0d0755356ee3c6390152ad
https://doi.org/10.1080/10618600.2018.1546592
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5
Hierarchically penalized quantile regression with multiple responses
Autor: Sungwan Bang, Jongkyeong Kang, Seung Jun Shin, Jaeshin Park
Publikováno v: Journal of the Korean Statistical Society. 47:471-481
We study variable selection in quantile regression with multiple responses. Instead of applying conventional penalized quantile regression to each response separately, it is desired to solve them simultaneously when the sparsity patterns of the regre
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::296971d466f6b95e5255f677e0dd4fb5
https://doi.org/10.1016/j.jkss.2018.05.004
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6
Electrical Load Forecasting Using Time Series Model
Autor: Sungwan Bang, Jaeshin Park
Publikováno v: The Korean Data Analysis Society. 19:3009-3018
현대 사회에서 삶을 영위하기 위하여 필수적인 요소인 전기는 많은 양을 저장할 수 없는 특성 때문에 정확한 전력수요를 바탕으로 적정량을 생산하는 것이 매우 중요하다. 원활한 전기 공
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a2a3bd73cc298860bb11a3a7e3f20b16
https://doi.org/10.37727/jkdas.2017.19.6.3009
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9
Hierarchically penalized support vector machine with grouped variables
Autor: Myoungshic Jhun, Jongkyeong Kang, Eunkyung Kim, Sungwan Bang
Publikováno v: International Journal of Machine Learning and Cybernetics. 8:1211-1221
When input features are naturally grouped or generated by factors in a linear classification problem, it is more meaningful to identify important groups or factors rather than individual features. The F ∞-norm support vector machine (SVM) and the g
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b0585018f5598496f09c782aea448749
https://doi.org/10.1007/s13042-016-0494-2
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10
Composite kernel quantile regression
Autor: Myoungshic Jhun, Sungwan Bang, Soo-Heang Eo, HyungJun Cho
Publikováno v: Communications in Statistics - Simulation and Computation. 46:2228-2240
The composite quantile regression (CQR) has been developed for the robust and efficient estimation of regression coefficients in a liner regression model. By employing the idea of the CQR, we propose a new regression method, called composite kernel q
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::71d4b147711c811eb1611b63cbcce9c0
https://doi.org/10.1080/03610918.2015.1039133
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