National Chengchi University, Social Science Center,Research & Development (Social network graph considers only
internal connections of NCCU
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LI-PANG CHEN (54)
2024-12
GUEST: an R package for handling estimation of graphical structure and multiclassification for error-prone gene expression data
2024-10
EATME: An R package for EWMA control charts with adjustments of measurement error
2024-09
Feature screening via concordance indices for left-truncated and right-censored survival data
2024-09
Accelerated failure time models with error-prone response and nonlinear covariates
2024-09
AteMeVs: An R package for the estimation of the average treatment effects with measurement error and variable selection for confounders
2024-09
Causal inference with high-dimensional error-prone covariates and misclassified treatments
2024-08
Variable selection and estimation for misclassified binary responses and multivariate error-prone predictors
2024-08
Estimation of graphical models: An overview of selected topics
2024-08
AFFECT: an R package for accelerated functional failure time model with error-contaminated survival times and applications to gene expression data
2024-06
Boosting method for length-biased and interval-censored survival data subject to high-dimensional error-prone covariates
2024-06
Analysis of Length-Biased and Partly Interval-Censored Survival Data with Mismeasured Covariates
2024-06
Variable selection and estimation for length-biased and partly interval-censored survival data with mismeasured covariates
2024-04
SIMEXBoost: An R package for analysis of high-dimensional error-prone data based on boosting method
2024-01
Unbiased boosting estimation for censored survival data
2023-12
Analysis of length-biased and partly interval-censored survival data with mismeasured covariates
2023-09
CHEMIST: an R package for causal inference with high-dimensional error-prone covariates and misclassified treatments
2023-08
A note of feature screening via a rank-based coefficient of correlation
2023-08
A new EWMA control chart for monitoring multinomial proportions
2023-08
A boosting method for variable selection and estimation with error-prone responses and predictors
2023-08
Variable selection and estimation for the average treatment effect with error-prone confounders
2023-07
BOOME: A boosting method for variable selection and estimation with measurement error in binary responses and predictors
2023-06
Boosting method for length-biased and interval-censored survival data subject to high-dimensional error-prone covariates
2023-05
Variable selection and estimation for the average treatment effect with error-prone confounders
2023-02
A new p-control chart with measurement error correction
2023-02
De-noising boosting methods for variable selection and estimation subject to error-prone variables
2023-02
Sentiment analysis and causal learning of COVID-19 Tweets prior to the rollout of vaccines
2023-02
Adjustment of Measurement Error Effects on Dispersion Control Chart with Distribution-Free Quality Variable
2023-01
Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders
2022-12
Measurement Error Correction for EWMA p-Charts
2022-12
Variable selection and estimation for misclassified responses and high-dimensional error-prone predictors
2022-12
Variable selection and estimation for the average treatment effect with error-prone confounders
2022-10
BOOME: A Python package for handling misclassified disease and ultrahigh-dimensional error-prone gene expression data
2022-09
Classification and prediction for multi-cancer data with ultrahigh-dimensional gene expressions
2022-07
De-noising analysis of noisy data under mixed graphical models
2022-07
[BOOK REVIEW] Handbook of Meta-Analysis
2022-06
Network-based discriminant analysis for multiclassification
2022-06
Nonparametric discriminant analysis with network structures in predictor
2022-06
Boosting method for length-biased and interval-censored survival data subject to high-dimensional error-prone covariates
2022-06
Network-based discriminant analysis for multiclassification
2022-04
[Book Review] Introduction to data science: Data analysis and prediction algorithms with R
2022-03
Sufficient dimension reduction for survival data analysis with error-prone variables
2022-01
Monitoring process location and dispersion simultaneously using a control region
2022-01
[Book Review] The ESD Control Program Handbook
2021-12
Supervised learning for binary classification on US adult income
2021-12
Network based discriminant analysis for multiclassification
2021-10
Quality control under nonparametric bivariate location and dispersion processes
2021-09
Analysis of noisy survival data with graphical proportional hazards measurement error models
2021-09
[Book Review] Statistical Foundations of Data Science
2021-06
Semiparametric methods for left-truncated and right-censored survival data with covariate measurement error
2021-04
[Book Review] Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
2021-04
Statistical learning for analysis of credit risk data
2021-01
Model-based forecasting for Canadian COVID-19 data
2020-11
Ultrahigh-dimensional sufficient dimension reduction for censored data with measurement error in covariates
2019-07
Support Vector Machine with Graphical Network Structures in Features
SU-FEN YANG
LI-PANG CHEN
Nation Chengchi University Library All Rights Reserved.
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