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Boosting method for length-biased and interval-censored survival data subject to high-dimensional error-prone covariates |
conference |
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| 2024-06 |
Analysis of Length-Biased and Partly Interval-Censored Survival Data with Mismeasured Covariates |
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| 2024-04 |
SIMEXBoost: An R package for analysis of high-dimensional error-prone data based on boosting method |
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| 2023-12 |
Analysis of length-biased and partly interval-censored survival data with mismeasured covariates |
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| 2023-10 |
Estimation of graphical models: An overview of selected topics |
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| 2023-09 |
CHEMIST: an R package for causal inference with high-dimensional error-prone covariates and misclassified treatments |
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| 2023-08 |
A note of feature screening via a rank-based coefficient of correlation |
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| 2023-08 |
A new EWMA control chart for monitoring multinomial proportions |
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| 2023-08 |
A boosting method for variable selection and estimation with error-prone responses and predictors |
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| 2023-08 |
Variable selection and estimation for the average treatment effect with error-prone confounders |
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| 2023-07 |
Variable selection and estimation for misclassified binary responses and multivariate error-prone predictors |
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| 2023-07 |
BOOME: A boosting method for variable selection and estimation with measurement error in binary responses and predictors |
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| 2023-06 |
Boosting method for length-biased and interval-censored survival data subject to high-dimensional error-prone covariates |
conference |
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| 2023-05 |
Variable selection and estimation for the average treatment effect with error-prone confounders |
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| 2023-02 |
A new p-control chart with measurement error correction |
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| 2023-02 |
De-noising boosting methods for variable selection and estimation subject to error-prone variables |
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Sentiment analysis and causal learning of COVID-19 Tweets prior to the rollout of vaccines |
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| 2023-01 |
Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders |
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| 2022-12 |
Measurement Error Correction for EWMA p-Charts |
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| 2022-12 |
Variable selection and estimation for misclassified responses and high-dimensional error-prone predictors |
conference |
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| 2022-12 |
Variable selection and estimation for the average treatment effect with error-prone confounders |
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| 2022-10 |
BOOME: A Python package for handling misclassified disease and ultrahigh-dimensional error-prone gene expression data |
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| 2022-09 |
Classification and prediction for multi-cancer data with ultrahigh-dimensional gene expressions |
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| 2022-07 |
De-noising analysis of noisy data under mixed graphical models |
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