selected Publications

Refereed Journal Papers 

*Master student; **PhD student; __Corresponding author

1. Chen, L.-P. and Lin, C.-K.**  (2024+). EATME: An R package for EWMA control charts with adjustments of measurement error. PLOS ONE. Accept.

2. Chen, L.-P. and Huang, H.-T.* (2024+). AFFECT: An R package for accelerated functional failure time model with error-contaminated survival times and applications to gene expression data. BMC Bioinformatics. In production.

3. Chen, L.-P. and Yi, G. Y. (2024+). AteMeVs: An R package for estimation of the average treatment effects with measurement error and variable selection for confounders. PLOS ONE. In production.

4. Chen, L.-P. and Hsu, W.-H.* (2024+). CHEMIST: An R package for causal inference with high-dimensional error-prone covariates and misclassified treatments. Japanese Journal of Statistics and Data Science (Invited submission for the special issue: Recent Advances in Biostatistics). To appear.

5. Chen, L.-P. (2024+). Estimation of graphical models:  An overview of selected topics. International Statistical Review. To appear.

6. Chen, L.-P. (2024). Feature screening via concordance indices for left-truncated and right-censored survival data. Journal of Statistical Planning and Inference, 232, 106153.

7. Chen, L.-P. (2024). Variable selection and estimation for misclassified binary responses and multivariate error-prone predictors. Journal of Computational and Graphical Statistics, 33, 407-420.

8. Chen, L.-P. and Yi, G. Y. (2024). Unbiased boosting estimation for censored survival data. Statistica Sinica, 34, 439-458.  

9. Chen, L.-P. and Qiu, B.* (2023). SIMEXBoost: An R package for  analysis of high-dimensional error-prone data based on boosting method. The R Journal, 15, 5-20.

10. Chen, L.-P. and Qiu, B.* (2023). Analysis of length-biased and partly interval-censored survival data with mismeasured covariates. Biometrics, 79, 3929-3940. 

11. Chen, L.-P. (2023). A note of feature screening via rank-based coefficient of correlation. Biometrical Journal, 65, 2100373.

12. Zhang, Q., Yi, G. Y., Chen, L.-P., and He, W. (2023). Sentiment analysis and causal learning of COVID-19 Tweets prior to the roll out of vaccines. PLOS ONE, 18(2): e0277878.

13. Yi, G. Y. and Chen, L.-P. (2023). Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders. Statistical Methods in Medical Research, 32, 691-711.

14. Chen, L.-P. (2023). De-noising boosting methods for variable selection and estimation subject to error-prone variables. Statistics and Computing, 33:38.

15. Chen, L.-P. and Yang, S.-F. (2023). A new p-chart with measurement error correction. Quality and Reliability Engineering International, 39, 81-98. 

16. Chen, L.-P. (2022).  BOOME: A Python package for handling misclassified disease and ultrahigh-dimensional error-prone gene expression data.  PLOS ONE, 17(10): e0276664.

17. Chen, L.-P. (2022). Classification and prediction for multi-cancer data with ultrahigh-dimensional gene expressions. PLOS ONE, 17(9): e0274440. 

18. Chen, L.-P. (2022). NetDA: An R package for network-based discriminant analysis subject to multi-label classes. Journal of Probability and Statistics. Article ID 1041752, 1-14. 

19. Chen, L.-P. and Yi, G. Y. (2022). De-noising analysis of noisy data with graphical models. Electronic Journal of Statistics, 16, 3861-3909. 

20. Chen, L.-P. (2022). Nonparametric discriminant analysis with network structures in predictor. Journal of Statistical Computation and Simulation, 92, 3836-3861. 

21. Chen, L.-P. (2022). Network-based discriminant analysis for multiclassification. Journal of Classification, 39, 410-431.

22. Chen, L.-P. and Yi, G. Y. (2022). Sufficient dimension reduction for survival data analysis with error-prone variables. Electronic Journal of Statistics, 16, 2082-2123. 

23. Chen, L.-P. and Yi, G. Y. (2021). Analysis of noisy survival data with graphical proportional hazards measurement error models. Biometrics, 77, 956–969. 

24. Chen, L.-P. and Yi, G. Y. (2021). Semiparametric methods for left-truncated and right-censored survival data  with covariate measurement error. Annals of the Institute of Statistical Mathematics, 73, 481–517. 

25. Chen, L.-P., Zhang, Q., Yi, G. Y., and He, W. (2021). Model-based forecasting for Canadian COVID-19 data. PLOS ONE, 16(1): e0244536. 

26. Chen, L.-P. (2021). Feature screening based on distance correlation for ultrahigh-dimensional censored data with covariates measurement error. Computational Statistics, 36, 857–884. 

27. Chen, L.-P. and Yi, G. Y. (2020). Model selection and model averaging for analysis of truncated and censored data with measurement error. Electronic Journal of Statistics, 14, 4054-4109. 

28. Chen, L.-P., Yi, G. Y., Zhang, Q., and He, W. (2019). Multiclass analysis and prediction with network structured covariates. Journal of Statistical Distributions and Applications (invited submission), 6:6. 

29. Chen, L.-P. (2019). Pseudo likelihood estimation for the additive hazards model with data subject to left-truncation and right-censoring. Statistics and Its Interface, 12, 135-148. 

Book Review Papers

1. Chen, L.-P. (2023). Statistical Inference and Machine Learning for Big Data by Mayer Alvo. Biometrics, 79, 4013-4013.

2. Chen, L.-P. (2023). Fundamentals of High-Dimensional Statistics: With Exercises and R Labs by Johannes Lederer. Biometrics, 79, 2772-2773.

3. Chen, L.-P. (2023). Statistical Inference and Machine Learning for Big Data by Mayer Alvo. Biometrics. In Production.

4. Chen, L.-P.(2022). Handbook of Measurement Error Models by Grace Y. Yi, Aurore Delaigle, and Paul Gustafson.  Biometrics, 78, 1269-1270.

5. Chen, L.-P. (2022). Introduction to Data Science: Data Analysis and Prediction Algorithms with R by Rafael A. Irizarry. Journal of the Royal Statistical Society, Series A, 185, 733-734. 

6. Chen, L.-P. (2022). Bayesian Analysis of Infectious Diseases: COVID-19 and Beyond by Lyle D. Broemeling. Journal of the Royal Statistical Society, Series A, 185, 729-730. 

7. Chen, L.-P. (2022). Handbook of Meta-Analysis by Christopher H. Schmid, Theo Stijnen, and Ian White. Biometrics, 78, 819-820. 

Twitter recommend: https://twitter.com/RobCalver5/status/1548983528221810689

8. Chen, L.-P. (2021). The ESD Control Program Handbook by  Jeremy M. Smallwood. Technometrics, 64, 148-149. 

9. Chen, L.-P. (2021). Statistical Foundations of Data Science by Jianqing Fan, Runze Li, Cun-Hui Zhang, and Hui Zou. Biometrics, 77, 1132-1135.

10. Chen, L.-P. (2021). Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python by Peter Bruce, Andrew Bruce, and Peter Gedeck. Technometrics, 62, 272-273. 

Conference Papers/Book Proceedings

1. Chen, L.-P. and Yi, G. Y. (2022). Robust feature screening for ultrahigh-dimensional censored data subject to measurement error. In: He, W., Wang, L., Chen, J., Lin, C.D. (eds)  Advances and Innovations in Statistics and Data Science (ICSA Book Series in Statistics). Springer, Cham, 23-53.

2. He, W., Yi, G. Y., and Chen, L.-P. (2019). Support vector machine with graphical network structures in features. Proceedings, Machine Learning and Data Mining in Pattern Recognition, 15th International Conference on Machine Learning and Data Mining, MLDM 2019, vol.II, 557-570.