Editorial. Design and Analysis of Experiments for Data Science
Volume 1, Issue 3 (2023), pp. 297–298
Pub. online: 27 November 2023
Type: Editorial
Open Access
Open Access
Published
27 November 2023
27 November 2023
References
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Chen, P.-Y., Chen, R.-B. and Wong, W. K. (2023). Particle Swarm Optimization for Finding Efficient Longitudinal Exact Designs for Nonlinear Models. The New England Journal of Statistics in Data Science 1–15. https://doi.org/10.51387/23-NEJSDS45.
Hao, S., Yang, M. and Zheng, W. (2023). Algorithm-Based Optimal and Efficient Exact Experimental Designs for Crossover and Interference Models. The New England Journal of Statistics in Data Science 1–10. https://doi.org/10.51387/23-NEJSDS41.
Kang, L., Deng, X. and Jin, R. (2023). Bayesian D-Optimal Design of Experiments with Quantitative and Qualitative Responses. The New England Journal of Statistics in Data Science 1–15. https://doi.org/10.51387/23-NEJSDS30.
Li, Y., Zhang, Q., Khademi, A. and Yang, B. (2023). Optimal Design of Controlled Experiments for Personalized Decision Making in the Presence of Observational Covariates. The New England Journal of Statistics in Data Science 1–8. https://doi.org/10.51387/23-NEJSDS22.
Pronzato, L. and Rendas, M.-J. (2023). Validation of Machine Learning Prediction Models. The New England Journal of Statistics in Data Science 1–21. https://doi.org/10.51387/23-NEJSDS50.
Qi, Y. and Chien, P. (2023). Construction of Supersaturated Designs with Small Coherence for Variable Selection. The New England Journal of Statistics in Data Science 1–11. https://doi.org/10.51387/23-NEJSDS34.
Shen, S., Mao, H., Zhang, Z., Chen, Z., Nie, K. and Deng, X. (2023). Clustering-Based Imputation for Dropout Buyers in Large-Scale Online Experimentation. The New England Journal of Statistics in Data Science 1–11. https://doi.org/10.51387/23-NEJSDS33.
Shi, C., Chiu, A. K. and Xu, H. (2023). Evaluating Designs for Hyperparameter Tuning in Deep Neural Networks. The New England Journal of Statistics in Data Science 1–8. https://doi.org/10.51387/23-NEJSDS26.
Singh, R. and Stufken, J. (2023). Subdata Selection With a Large Number of Variables. The New England Journal of Statistics in Data Science 1–13. https://doi.org/10.51387/23-NEJSDS36.
Zhu, H., Yu, J., Lai, D. and Wang, L. (2023). Seamless Clinical Trials with Doubly Adaptive Biased Coin Designs. The New England Journal of Statistics in Data Science 1–9. https://doi.org/10.51387/23-NEJSDS25.
