Theory and methodology

Pre-prints

• Smucler E., Rotnitzky A. and Robins J.M. (2019): A unifying approach for doubly-robust l1 regularized estimation of causal contrasts. Available here.

Published

• Smucler E. and Rotnitzky A. (2022): A note on efficient minimum cost adjustment sets in causal graphical models. Journal of Causal Inference. Available here.
• Smucler E., Sapienza F. and Rotnitzky A. (2022): Efficient adjustment sets in causal graphical models with hidden variables. Biometrika. Available here.
• Pinasco D., Smucler E. and Zalduendo I. (2021): Orthant probabilities and the attainment of maxima on a vertex of a simplex. Linear Algebra and its Applications. Available here.
• Rotnitzky A., Smucler E. and Robins J.M. (2021): Characterization of parameters with a mixed bias property. Biometrika. Available here.
• Rotnitzky A. and Smucler E. (2020): Efficient adjustment sets for population average treatment effect estimation in non-parametric causal graphical models. Journal of Machine Learning Research. Available here.
• Peña, D., Smucler, E. and Yohai V.J. (2020): Sparse estimation of dynamic principal components for forecasting high-dimensional time series. International Journal of Forecasting. Available here.
• Peña, D., Smucler, E. and Yohai V.J. (2020): gdpc: an R Package for Generalized Dynamic Principal Components. Journal of Statistical Software. Available here.
• Christidis A., Lakshmanan, L., Smucler E. and Zamar R. (2020): Split Regularized Regression. Technometrics. Available here.
• Peña, D., Smucler, E. and Yohai V.J. (2019): Forecasting Multiple Time Series with One-Sided Dynamic Principal Components. Journal of the American Statistical Association. Available here.
• Smucler, E. (2019): Consistency of Generalized Dynamic Principal Components in Dynamic Factor Models. Statistics & Probability Letters. Available here.
• Cohen Freue, G.V, Kepplinger, D., Salibian-Barrera, M. and Smucler, E. (2019): Robust elastic net estimators for variable selection and identification of proteomic biomarkers. Annals of Applied Statistics. Available here.
• Smucler, E. (2019): Asymptotics for Redescending M-estimators in Linear Models with Increasing Dimension. Statistica Sinica. Available here.
• Forzani, L., Rodriguez, D., Smucler, E. and Sued, M. (2019): Sufficient dimension reduction and prediction in regression: asymptotic results. Journal of Multivariate Analysis. Available here.
• Bura, E., Duarte, S., Forzani, L., Smucler, E. and Sued, M. (2018): Asymptotic Theory for Maximum Likelihood Estimates in Reduced-Rank Multivariate Generalized Linear Models. Statistics. Available here.
• Smucler, E. and Yohai, V.J. (2017): Robust and Sparse Estimators for Linear Regression Models. Computational Statistics & Data Analysis. Available here.
• Smucler, E. and Yohai, V.J. (2015): Highly Robust and Highly Finite Sample Efficient Estimators for the Linear Model. In Modern Nonparametric, Robust and Multivariate Methods: Festschrift in Honour of Hannu Oja (Nordhausen, K. and Taskinen, S., eds.). Springer, New York.

Applications

• Somacal A., Boechi, L., Jonckheere M., Lefieux, V., Picard, D. and Smucler E. (2022): Uncovering differential equations from data with hidden variables. Accepted at Physical Review E. Arxiv version available here.
• Barrera, Y., Boechi, L., Jonckheere, M., Lefieux, V., Picard, D., Smucler, E., Somacal, A. and Umfurer, A. (2021). Clustering high dimensional meteorological scenarios: Results and performance index. International Journal of Approximate Reasoning.
• Gianatiempo, O., Sonzogni, S. V., Fesser, E. A., Belluscio, L. M., Smucler, E., Sued, M. R., & Cánepa, E. T. (2018). Intergenerational transmission of maternal care deficiency and offspring development delay induced by perinatal protein malnutrition. Nutritional Neuroscience.
• Grings, F., Bruscantini, C., Smucler, E., Carballo, F., Dillon, M.E., Collini, E., Salvia, M. and Karszenbaum, H. (2015): Validation Strategies for Satellite-Based Soil Moisture Products Over Argentine Pampas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.