ScholarWorks@Korea University: CHO, HYUNG JUN

Detailed Information

CHO, HYUNG JUN (조형준)

College of Political Science & Economics (Department of Statistics)

Research Area

Tel 02-3290-2242
Homepage

ResearcherID ORCiD Scopus Google Scholar ID ResearchGate

Keyword

decision tree:5||데이터마이닝:5||의사결정나무:5||Prognosis:4||Kernel:3||Pancreas:3||Regression tree:3||quantile regression:3||CART:3||Data mining:3||GUIDE:3||CART.:2||EBAM:2||data mining:2||PRIM:2||SAM:2||Noncrossing:2||Regularization:2||partial least squares:2||Metastasis:2||boosting:2||배깅:2||Lymph node:2||부스팅:2||Vascular endothelial growth factor:2||FDR:2||Pancreatic ductal adenocarcinoma:2||Multiple quantile regression:2||Size:1||Selection bias:1||recurrence:1||점수동등화:1||공정성:1||standardized score:1||범프탐색:1||계층적 군집분석.:1||mixed data:1||distance measure:1||regression tree.:1||scientific military training:1||sample size:1||Ensemble:1||혼합효과모형.:1||gene expression data:1||연속형 예측변수:1||classification analysis:1||censored data:1||modal linear regression:1||Venous invasion:1||time series forecasting:1||STL decomposition:1||Noncrossing:1||표준화점수:1||백분위점수:1||fairness:1||예측력:1||hybrid model:1||hierarchical clustering.:1||SCP3:1||time series data:1||timevarying covariate.:1||scientific battle training:1||Formalinfixed paraffinembedded:1||credit scoring model:1||multiple quantile regression:1||factorization estimation:1||Count data:1||Bootstrap:1||Microarray:1||R:1||treestructured method:1||Markov chain Monte Carlo (MCMC):1||Ki67:1||변수 선택.:1||significance:1||variable selection.:1||multivariate linear mixed effects model:1||Liver:1||Gastric:1||Principal component analysis:1||혼합 모형:1||거리 측정:1||Lymphangiogenesis:1||Lymph node metastasis:1||Nonsmall cell lung cancer:1||outlier detection:1||Supnorm:1||Variable selection:1||비교차:1||유의한 유전자:1||microarray 실험:1||Adjustment pvalues:1||Empirical Bayes:1||likelihood:1||Data Mining:1||이항반응변수:1||empirical Bayes (EB):1||Neuroendocrine tumor:1||앙상블 기법:1||유의성:1||Ampulla of Vater:1||커널.:1||Multicollinear:1||Pancreatic intraepithelial neoplasia:1||시계열분석:1||nonparametric method:1||Uncertainty coefficient:1||bump hunting:1||혼합형 자료:1||multivariate data analysis:1||Ridge regression:1||Multiple quantiles regression:1||adaptive lasso:1||과학화 전투훈련:1||경시적 이항 자료:1||ordinal multinomial data:1||Multivariate methods:1||mixed outcomes:1||pancreatic intraepithelial neoplasia:1||Significant genes:1||표본수:1||Bagging:1||microarray data:1||Classification Analysis:1||recursive partitioning:1||Bayesian false discovery rate (FDR):1||random forests:1||variable importance:1||predictive variable:1||modal decision tree:1||kernel.:1||Survival:1||Neoplasms:1||STL 분해:1||Fourier 변수:1||cancer:1||KRAS:1||pancreas:1||efficiency.:1||nominal variable:1||inverse censoring probability:1||시간종속변수.:1||Pancreatic cancer:1||Mixedeffects model:1||Immunohistochemistry:1||Reversephase protein array:1||residual analysis.:1||신용 리스크:1||Credit risk:1||credit risk management:1||다중 분위수 회귀:1||acinartoductal metaplasia:1||pancreatic cancer:1||Interval censoring:1||microarray experiment:1||효과의 크기:1||Markov chain Monte Carlo:1||dummy variable transformation:1||nominal predictor variable:1||부분집단 분석:1||subgroup analysis:1||Mucinous cystic neoplasm:1||Tumorigenesis:1||Tertiary lymphoid structure:1||머신러닝.:1||margin:1||survival:1||커널 추정.:1||kernel estimation.:1||효율성.:1||penalized regression:1||censoring:1||endogenous variable:1||Cox proportional hazard model:1||Recursive partitioning:1||Colon cancer:1||잔차분석:1||혼합 반응변수.:1||risk factors:1||prognosis:1||telomere:1||조정유의확률:1||reproducibility:1||앙상블기법:1||데이터 마이닝:1||bagging:1||Microarray data:1||No replication:1||survival data:1||robustness:1||분류분석:1||median regression:1||heteroscedastic error:1||correlation:1||가변수 변환:1||continuous predictor variable:1||중도절단:1||Grading:1||Synaptophysin:1||회귀모형:1||ensemble technique:1||regression model:1||최빈값 의사결정나무:1||complex nonlinear relationships:1||Telomere:1||machine learning.:1||threedimensional image:1||seemingly unrelated regression:1||selection bias:1||percentile rank score:1||prediction accuracy:1||다변량 자료분석:1||분위수 회귀모형:1||회귀나무 모형:1||Nonparametric estimation:1||quantile autoregressive model:1||Right censored data:1||variable selection:1||joint models for longitudinal and timetoevent data:1||hypercholesterolemia:1||longitudinal binary data:1||커널:1||noncrossing:1||GEE.:1||Decision tree:1||계수형자료:1||Median regression:1||재현성:1||Binary Response Variable:1||Mixed effects Model.:1||분류 분석:1||Cancer:1||Neuroendocrine carcinoma:1||Molecular subgroup:1||fourier term:1||score equating:1||명목형 변수:1||Composite quantile regression:1||composite quantile regression:1||생존 및 경시자료의 결합모형:1||내생변수:1||CTREE:1||Tumor burden:1||T classification:1||mixed response data:1||Heart failure:1||순서형 다항 자료:1||신용평점 모형:1||이차함수 계획법.:1||quadratic programming.:1||Missing information principle:1||Robustness:1||마이크로어레이:1||effect size:1||앙상블:1||ensemble:1||accelerated failure time model:1||명목형 예측변수:1||정밀 의료:1||예측 변수:1||Precision medicine:1||random effect:1||Epithelialmesenchymal transition:1||최빈값 선형회귀:1||

RSS_1.0 RSS_2.0 ATOM_1.0

145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963

Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.

Detailed Information

Keyword

BROWSE