Pushing the Boundaries of Data Science Through Statistical Modelling and Inference: Highlights from a Special Issue
Volume 4, Issue 1 (2026), pp. 1–4
Pub. online: 29 September 2026
Type: Editorial
Open Access
Open Access
Published
29 September 2026
29 September 2026
References
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Fengler, M. R., Jäger, B. and Okhrin, O. (2025). Locally Adaptive Modeling of Unconditional Heteroskedasticity. The New England Journal of Statistics in Data Science 105–130. https://doi.org/10.51387/25-NEJSDS91.
Ma, S., Yan, J. and Zhang, X. (2026). Extreme Value Modeling with Generalized Pareto Distributions for Rounded Data. The New England Journal of Statistics in Data Science 37–48. https://doi.org/10.51387/26-NEJSDS101.
Ndreka, B., Dey, D. K. and Lachos, V. H. (2026). Bayesian Estimation of Contagion Effect: An Application of Friendship Networks and Alcohol Behavior. The New England Journal of Statistics in Data Science 77–89. https://doi.org/10.51387/26-NEJSDS94.
Odoom, E. and Wang, X. (2026). Consistent and Scalable Variable Selection with Robust Link Functions. The New England Journal of Statistics in Data Science 90–104. https://doi.org/10.51387/26-NEJSDS102.
Sahoo, I., Majumder, S., Hazra, A., Rappold, A. G. and Bandyopadhyay, D. (2025). Computationally Scalable Bayesian SPDE Modeling for Censored Spatial Responses. The New England Journal of Statistics in Data Science 49–63. https://doi.org/10.51387/25-NEJSDS78.
Su, W., Wang, X., Miranda-Afonso, P., Andrinopoulou, E.-R. and Szczesniak, R. D. (2026). Modeling Disease Progression in the Presence of an Outcome-Dependent Visiting Process with Application to Cystic Fibrosis Clinical Data. The New England Journal of Statistics in Data Science 5–17. https://doi.org/10.51387/26-NEJSDS99.
